Broad S treet Scientific Volume 2 | 2012-2013
The North North Carolina Carolina School School of of Science Science and and The Mathematics Journal of Student STEM Research
ic Volume 2 | 2012-2013
The North Carolina School of Science and Mathematics Journal of Student STEM Research
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TABLE OF CONTENTS vi
Broad Street Scientific Staff
1
A Letter from the Chancellor
2
A Photosynthetic City: Combining Nature with the Urban Environment Emmanuel Assa, 2013
4
Effects of Climate Change on Agriculture Matias Horst, 2014 Vivek Pisharody, 2014
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A Novel Design of Electrode Surface Morphology to Improve Water Electrolysis Efficiency
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Jaehyeong Lee, 2013
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Multilevel Distance Labeling - A Wireless Network Problem Tian-Shun Allan Jiang, 2014
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The Effect of Substrate Density on the Rate of Migration of NIH3T3 Fibroblasts Elizabeth Tsui, 2013
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Chitosan-Modified Cellulose as Adsorbent to Collect and Reuse Nitrate from Groundwater Christie Jiang, 2013
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Generation of Electricity from the Wind Draft of Cars
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Harish Pudukodu, 2013
Shocking Discoveries: The Applications and Putative Mechanisms of the Effects of Electric and Magnetic Fields on Plants
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Ian Maynor, 2013
Halobacterium: Mechanisms of Extreme Survival as a Solution to Waste
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Isaiah Stackleather , 2013
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Kanan Shah, 2014 Vivek Pisharody, 2014
Intervertebral Discs and Their Interactions with Different Environments
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Alzheimer’s Disease: Current Therapies and Emerging Research
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Jin Yoon, 2013
Effect of Backpack Load on Gait Parameters Alice Li, 2014
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intERviEw
An Interview with Dr. Robert Lefkowitz
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Words from the Editors Welcome to the Broad Street Scientiic: NCSSM’s journal of student research in science, technology, engineering, and mathematics. In this second edition of the Broad Street Scientiic, we aim to not only showcase student research, but to increase public awareness of the importance of student scientiic participation by demonstrating the scientiic aptitude of our students to readers both in and outside of the NCSSM community. We hope you enjoy this year’s issue. he editors have chosen the theme of astrophysics, a fundamental observational science which deals with the study of astronomical objects. We appreciate he Hubble Key Project Team (NASA) for the cover photo, an image of supernova 1994D in the galaxy NGC 4526, and he Hubble Heritage Team (NASA) for the back cover photo, an image of the planetary Ring Nebula. he edition is sectioned by the type of manuscript, which includes papers, literature reviews, essays, and an interview. Each section has a diferent side bar image of the sun. here are green, orange-red, blue, and yellow images – each color corresponds to images taken with ilters of diferent wavelength. he diferent colors depict diferent atmospheric temperatures of the sun, as hotter atmospheric temperatures emit more blue light and cooler parts emit more red light. he back pages of the issue shows the Hubble telescope, which has been for two decades one of the most important instruments to astronomy. he editors would like to thank the administration, faculty, and staf of NCSSM for the opportunity to pursue our research goals in any of the science, technology, engineering or mathematics ields. he support for student research at this school is unparalleled by any other high school in the state, and the student body would like to recognize the signiicance of such an investment in our, and the state’s, future. We would like to speciically thank our faculty advisor, Dr. Jonathan Bennett, for his advice and guidance through the second edition of the Broad Street Scientiic. We would also like thank our Chancellor, Dr. Roberts, for his active support of this publication. he Broad Street Scientiic would like to thank Hun Wong and Pranav Maddi, last year’s chief editors, for their helpful recommendations and also Navina Venugopal, who assisted with the art and cover design. Lastly, the Broad Street Scientiic is extremely grateful to Dr. Robert Lefkowitz for his participation in this journal and insight for the next generation of scientists.
BroadStreetSci Online
www.ncssm.edu/bss Volume 2 | 2012-2013 |
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Broad Street Scientific Staff Chief Editors
Halston Lim, 2013 Tejas Sundaresan, 2013
Publication Editors
Vincent Cahill, 2013 Addie Jackson, 2014 Andrew Peterson, 2014 Anita Simha, 2013 Wey-Wey Su, 2013
Biology Editors
Adam Beyer, 2014 Katherine Whang, 2013
Physics Editors
Jason Liang, 2013 Jessica Lee, 2014
Chemistry Editors
William Ge, 2013 Parth hakker, 2014 Christopher Yuan, 2014
Engineering Editor
Madeline Finnegan, 2014
Math and Computer Science Editor
Kavi Jain, 2014
Webmaster
Kyle Elmore, 2013
Faculty Advisor
Dr. Jonathan Bennett
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Letter from the Chancellor ”Equipped with his five senses, man explores the universe around him and calls the adventure Science.” ~ Edwin Hubble I am proud to introduce the second edition of the North Carolina School of Science and Mathematics (NCSSM) scientiic journal, Broad Street Scientiic. Each year students at NCSSM conduct signiicant scientiic research and Broad Street Scientiic is a showcase of some of the best research being done by students at NCSSM. Providing students with opportunities to apply their learning through research is not only vitally important in preparing and exciting students to pursue STEM degrees and careers after high school, but essential to encouraging innovative thinking that allows students to scientiically address major challenges and problems we face in the world today and will face in the future. Opened in 1980, NCSSM was the nation’s irst public residential high school where students study a specialized curriculum emphasizing science and mathematics. Teaching students to do research and providing them with opportunities to conduct high-level research in biology, chemistry, physics, the applied sciences, math, and the social sciences is a critical component of NCSSM’s mission to educate academically talented students to become state, national and global leaders in science, technology, engineering and mathematics. NCSSM continues to expand real world opportunities for students through our research and mentorship programs. Over the past two years we have doubled the number of these opportunities and look forward to continuing to provide our students with the type of experiences that lead to the outstanding learning relected in Broad Street Scientiic. he research showcased in this publication is an example of the signiicant research that students conduct each year at NCSSM under the direction of the outstanding faculty at our school and in collaboration with researchers at major universities. For twenty-seven years NCSSM has showcased student research through our annual Research Symposium each spring and at major research competitions such as the Siemens Competition in Math, Science and Technology, the Intel Science Talent Search, Toshiba Exploravision, and the International Science and Engineering Fair to name a few. he publication of Broad Street Scientiic provides another opportunity to highlight the outstanding research being conducted by students each year at the North Carolina School of Science and Mathematics. I would like to thank all of the students and faculty involved in producing Broad Street Scientiic, particularly faculty sponsor Dr. Jonathan Bennett and senior editors Tejas Sundaresan and Halston Lim. Explore and Enjoy! Sincerely, Dr. Todd Roberts, Chancellor North Carolina School of Science and Mathematics
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A Photosynthetic City: Combining Nature with the Urban Environment Emmanuel Assa Emmanuel Assa was selected as the winner of the 2012-2013 Broad Street Scientiic Essay Contest. His award included the opportunity to interview Dr. Lefkowitz as part of the Interview section of the journal. Modern cities are not perfect. As they expand, they become centers for human civilization, but also centers for civilization’s greatest problems. As more and more of the human population moves into the city, the space we need increases, causing severe urban sprawl [1]. Urban sprawl creates the Urban Heat Island Efect, along with air and water pollution [1, 2]. he Urban Heat Island Efect, in turn, increases the cost of living, reduces a city’s comfort level, and can have detrimental impacts on a city’s surrounding environment [1]. To improve this situation, many researchers have proposed the added introduction of vegetation into the urban environment [2]. his practice will diminish pollution, reduce the Urban Heat Island, and even produce a proit for the city [2]. Integrating nature into the urban will produce better, cleaner, and more cost-efective cities. For any city, pollution can be a major problem, especially in the air. Smog is a common phenomenon around large cities. Smog, a noxious combination of smoke and fog, contains chemicals such as sulfur dioxide and nitrogen oxides, key compounds in acid rain formation [2]. hese chemicals are both harmful to the lungs and odorous, lowering a city’s residential appeal. Introducing plants into this environment would solve the problem almost immediately. Many types of vegetation absorb those harmful airborne chemicals, preventing them from forming acid rain [2]. hese plants would efectively clean the surrounding air, lowering the chance of acid rain and improving the health of the city residents. he Urban Heat Island is one of the most-studied effects of the current urban sprawl, and they key to the problem lies in albedo, the relative relectivity of a material [3]. he higher an object’s albedo, the more light it relects. Concrete and asphalt, the primary modern construction building materials, have very low albedo [3]. his means that during the day, the concrete and asphalt in cities absorb radiation from sunlight, and then release it at night as heat [3]. his creates a heat “bubble” around a city. Because of the increased temperature, air conditioning systems in every building inside of this “bubble” must consume more energy in order to maintain a comfortable interior temperature. More energy consumed means more energy bought from the power companies, which means a higher mainte2012-2013| Volume | Volume 2 |2 |2012-2013 2 2
nance cost [1]. his property of the Urban Heat Island is the most economically threatening [3]. he solution is still to introduce vegetation into the urban environment. he leaves of a plant have a much higher albedo than pavement or concrete, and plants release only a small amount of infrared radiation in the form of heat [2]. he more surface area of a city occupied by pants, the less severe the Urban Heat Island. hrough this efect, planting trees and other vegetation can reduce the costs of building maintenance within a city. he other economic beneit of integrating nature with the urban comes from the visual appeal of a city. A better-looking city can charge more for building space and property taxes, supplementing the city’s economy. Vegetation makes a city more pleasant to live in because it provides color against the normal grays and blacks of concrete and asphalt, and the shade it provides during the warmer months makes the city a more attractive place to live. Green roofs are possible ways of integrating nature with the urban environment. A green roof is similar to a patch of vegetation that covers the top of a building. It increases the albedo of the building (lowering the Urban Heat Island Efect), and the vegetation can be drought-tolerant, minimizing the amount of water the owner would need to use to maintain it [4]. However, this limits the selection of plants available for the roof. Green roofs can be made to be aesthetic as well as functional; tropical or exotic vegetation can be added if the area is intended for recreation [4]. Precautions for aesthetic green roofs are that extra structural support is needed to hold up the extra weight, and exotic plants may require additional maintenance [4]. Both types of green roofs are capable of increasing a roof membrane’s longevity, improving a building’s sound insulation, reducing a building’s energy costs, and reducing the rainwater runof [4]. Plain green roofs are cheap and easy to install, but aesthetic green roofs require planning in advance so that additional supports can be set up within the building shell. he typical cost of a green roof is only around $100 to $300 per square meter. Green roofs are an easy and eficient way to incorporate nature into its city surroundings. Many of the problems caused by urban sprawl can be either reduced or eradicated by introducing vegetation into the urban environment. All at once, it can improve
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air quality, reduce the cost of living in an urban area, and create a beautiful cityscape. Implementing this concept on a city-wide scale also requires very little capital. By maintaining a careful balance between nature and human constructions, we can accommodate the increasing urban population with ease.
References [1] Golden, J. S. (2004). he built environment induced urban heat island efect in rapidly urbanizing arid regions – a sustainable urban engineering complexity. Environmental Sciences, 1(4), 321-349 [2] Manning, W. J. (2008). Plants in urban ecosystems: Essential role of urban forests in urban metabolism and succession toward sustainability. International Journal of Sustainable Development and World Ecology, 15(4), 362370. http://search.proquest.com/docview/197928423?acc ountid=12723 [3] Hecht A., Fiksel J., Fulton S., Yosie T., Hawkins N., Leuenberger H., Golden J., & Lovejoy T. 2012. Rejoinder: Creating the future we want. Sustainability: Science, Practice, & Policy 8(2) Published online Apr 20, 2012. http:// www.google.com/archives/vol8iss2/1203-002.rejoinder. html [4] Oberndorfer, E., Lundholm, J., Bass, B., Cofman, R. R.; et al. (2007). Green roofs as urban ecosystems: Ecological structures, functions, and services. Bioscience, 57(10), 823-833.
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Effects of Climate Change on Agriculture Matias Horst and Vivek Pisharody Without a doubt, global climate change presents a serious threat to agricultural productivity. Current data indicate that immediate, directed action is necessary to protect world food security. Unfortunately, political conlicts regarding climate change have hindered the development of solutions to these issues. However, there are numerous innovative methods that have been proposed to combat the negative impacts of climate change on agriculture regardless of international unwillingness to address the problem itself. Agriculture is perhaps the single human activity most closely tied to climate. However, evaluating the impact of global climate change on agriculture presents a diiculty in that while climate change occurs on the global scale, impacts on agriculture occur at the local level, with considerable variation between diferent regions. In their analysis, Kurukulasuriya and Rosenthal predict a modest net decrease in world agricultural output. Decreased yields in some regions will slightly outweigh productivity gains in other regions. However, the real threat to world food security arises not from this net decrease, but from the distribution of climate related efects on agriculture [1]. he true challenge of dealing with climate change’s efects on agriculture lies in tailoring unique solutions to speciic regions and their respective climates. In many areas, climate change has already reduced agricultural yields. As ocean temperatures rise, meltwater from mountain and Antarctic glaciers has caused an increase in sea level, threatening to engulf and destroy productive ields in low-lying areas. While climate change in some regions of the world may reduce yields through looding, other regions are rapidly losing arable land because of severe drought. In the tropics and subtropics, rainfall levels are dropping, and droughts have increased in duration, decreasing crop yields [2]. In highland regions, frost damage due to increased CO2 concentrations has similarly impacted production. However, in certain regions, agriculture productivity may actually rise. In high latitudes, lengthened growing seasons can augment agricultural productivity. Similarly, at high altitudes, higher temperatures may make more land suitable for farming [1]. Furthermore, increased concentrations of CO2 can make water use and photosynthesis more eicient. he simplest method of adapting to changes in speciic environments is modiication of current farming techniques. In semi-arid regions, increasing rates of desertiication have disrupted local ecosystems. Reduced rainfall, coupled with topsoil erosion due to wind, have reduced agricultural yields in the Middle East and sub-Saharan Africa [3]. A group of leading Israeli scientists has gen4 | 2012-2013 | Volume 2
erated mathematical models to optimize vegetation coverage of sand dunes. Changing surface cover can modify local microclimates by afecting wind speed, surface humidity, and absorbed radiation levels. Techniques aimed at reducing grazing stress can halve the number of mobile dunes, decreasing exposed sand surface area and thereby facilitating local botanic agriculture and increasing local water levels. he study also revealed that, in areas where precipitation is suicient, breaking up moss and bacteria layers on the soil can induce vegetation growth and cancel local desertiication [3]. In temperate environments, heat and frost damage are major concerns. If plants mature at too early a time, they will be susceptible to damage from summer temperature peaks and associated dehydration. Furthermore, plants may yield crops earlier in the year as a result of heat stress. As spring temperatures rise, seedlings begin to emerge prior to the last frost. Increasing frost damage presents a serious challenge to agriculture in same extreme latitudes and higher altitudes in which climate change is expected to increase yields [4]. A method that has been useful in combating both of these issues is genetic engineering. he responses of plants to stress can be strengthened by amplifying the chemical signals between the chloroplasts or mitochondria, the organelles that most rapidly detect stress, and the nucleus. Scientists have discovered epigenetic procedures to artiicially induce early crop yields as a means of adapting to shorter growing seasons. Gene splicing techniques using small fragments of RNA can also be used to inluence lowering time; if lowering time is delayed, then most frost damage may be avoided. he transfer of genes from one species to another, transgenics, proves to be an opportunity to adapt the environmental strengths of some species to the conditions that other environments develop as a result of climate change. Heat, cold, and even salinity resistance can be provided by certain combinations of DNA [4]. A prominent example of a recombinant organism designed to combat frost damage is the frost-resistant strawberry grown throughout North Carolina. By inserting genes from the Winter Flounder, which produces anti-freeze compounds to survive in frigid waters, into the genes of a common strawberry cultivar, a strawberry highly resistant to frost was developed [5]. Over the past several decades, agricultural practices have become increasingly homogenous, while environments have become increasingly fractured and diversiied. As traditional agricultural practices are overturned in favor of new methods and heirloom seeds are discarded in favor of a few high-yield varieties, there is a severe risk of losing
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biodiversity. his potential loss of biodiversity represents a serious threat to future food security by constricting agriculture to a few popular, widespread species, an especially dangerous issue at a time when environmental stresses are diversifying. Additionally, lost biodiversity can reduce the potential of genetic engineering by reducing the availability of genes for transfer. In response, numerous seed banks exist throughout the world, the most prominent of which is in Svalbard, Norway and contains 775,000 samples from 231 countries stored at -18ºC [6]. Solving the issue of anthropogenic climate change by addressing the root cause – CO2 emissions – has been hindered by challenging economic and political issues outside the scope of science. Despite these challenges, it is possible to face the problems caused by climate change through innovative scientiic solutions. he impacts of climate change are diverse, and range from devastating to beneicial. By addressing these issues within the context of their local environments, scientists can mitigate problems and take advantage of new opportunities created by diferent environmental conditions.
References [1] Kurukulasuriya, P., & Rosenthal, S. (2003). Climate Change and Agriculture.Firsov, A. P., & Dolgov, S. V. (1998). [2] World Meteorological Organization. (n.d.). Climate change and desertiication. [3] Kinast, S., Meron, E., Yizhaq, H., & Ashkenazy, Y. (2012). Biogenic crust dynamics on sand dunes. Biological Physics; Geophysics. [4] Mittler, R., & Blumwald, E. (2010). Genetic engineering for modern agriculture: challenges and perspectives. Annual review of plant biology, 61, 443–62. [5] Agrobacterial Transformation and Transfer of the Antifreeze Protein Gene Of Winter Flounder to the Strawberry. [6] Food, M. of A. and. (2007, April 3). Svalbard Global Seed Vault. regjeringen. no.
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A Novel Design of Electrode Surface Morphology to Improve Water Electrolysis Eficiency Jaehyeong Lee ABSTRACT A new surface morphology was proposed in this study to optimize the eiciency of water electrolysis. Past studies have shown that reducing particle size is less eicacious in improving electrolysis eiciency than modifying surface morphology. Using Ni metal and a speciied pattern thickness, along with a novel ilm pattern size, the design proposed in this study has ~13.4% more efective surface area than a simple pattern with straight side walls. To realize the proposed surface morphology, photoresist patterned Ni electroplating was used. he surface morphology of the photoresist and resulting plated Ni ilm were conirmed by a scanning electron microscope (SEM). To improve the accuracy of the measurement, the Kelvin probe method was used with a specially designed sample holder to reduce the efect of contact resistivity and external resistance of the system. For Ni electrode test, Ag/ AgCl in 4 M KCl solution was used as a reference electrode and Pt was used as counter electrode. For quantitative analysis of the surface area efect, sputtered Ni ilm was tested with Telon tape as a masking material to deine the active area of the ilm. he test system was observed to accurately detect the efect of bubble accumulation on the ilm surface with a narrow trench like opening. he Voltammogram was analyzed using a modiied Butler-Volmer equation with series resistance. A data analysis program was written to ind resistance, rs (Ω), exchange current density, J0 (Amps/cm2), and the charge transfer coeicient, α. his new analysis method was compared to a conventional method from literature in order to ensure validity. he results showed that, using the proposed surface morphology modiication, the series resistance decreased 20.4% from its “expected” value – which then translates into a 25.6% increase in eiciency at a given bias voltage.
Introduction Today, the majority of the world runs on a hydrocarbonbased fuel supply. he source of this energy, however, is in fossil fuels, energy rich hydrocarbons that lie dormant under select regions of the Earth. hough energy eiciency is high for fossil fuels, the carbon emissions can cause many environmental concerns. In addition, sustainable forms of energy are currently not cost competitive against fossil fuels: data from the Henry Hub natural gas distribution system versus data from the Norwegian University of Science and Technology (NTNU) in 2006 conirms the relatively high cost eiciency of natural gas, as the cost per million BTU of natural gas in December was around $6.734/Mil. BTU, and NTNU reported the cost per kilowatt hour to produce electrolytic hydrogen at $0.10/kWh (or $29.31/ Mil. BTU) [1]. herefore, research in improving the eficiency of sustainable energy production, such as water electrolysis, is of critical importance. here are two methods to improve the eiciency of water electrolysis: employ a metal with greater catalytic properties or increase the efective surface area of the electrode. Research on the catalytic properties of non-noble metals in water electrolysis has been undertaken for over two centuries in search of cheaper metals with greater catalytic efects. Non-noble metals observed include cobalt, manganese, nickel, iron, copper, chromium, vanadium, and their alloys and oxides [2-9]. More recently, diferent electrode surface modifying techniques have been designed and tested to improve the hydrogen generation eiciency of water electrolysis. hese 6 | 2012-2013 | Volume 2
techniques include building nanowire arrays, nanoparticles, nanocrystals, nanotubes, nanoholes, structures with micropores, and three-dimensional dendrite formation structures using high current electroplating methods [1015]. However, more recent advances in surface morphology modiication focus on nanowire and nanotube growth because the efective surface area of an electrode greatly increases with increasing aspect ratio between the height of the structure and its cross sectional area. However, those earlier publications focus mostly on fabrication methods for unique nanostructures rather than on a rigorous analysis on the eiciency of the electrolysis or on a quantitative analysis of the impact of the aspect ratio to the surface area [10-15]. his paper will quantitatively analyze the efective surface area as a function of aspect ratio. Based on this calculation, it was found that the aspect ratio had greater impact to the eiciency than individual particle sizes. he primary focus of this research is to ind a simple way to increase the surface area even further with a given aspect ratio. A new electrode surface morphology involving curved sidewalls to make a structure with a protruding top, or mushroom top, was designed and tested, which further increased the efective surface area of an electrode compared to the straight side wall structure when the aspect ratio is same. In particular, the method used to produce the structure was economically viable, using existing technologies such as photoresist photolithography and electroplating. his apparently simple and easy method to produce electrodes for more efective water electrolysis has not
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been tried before, based on an extensive literature search undertaken through an online library covering over 500 published materials for the last 60 years. Background heory Cyclic voltammetry was used to collect the current densities of an electrolysis system consisting of cathode and anode electrodes over a range of voltages with a deined rate of voltage change per unit time. he current voltage characteristics obtained from cyclic voltammetry are called voltammograms. To analyze the curve of the voltammograms, a Butler Volmer equation with an Ohmic limiting resistance component was used [44]. According to the Nernst equation, the standard electrode potential of the cathode (Ec0) and anode (Ea0) can be expressed as a function of pH of a solution and the partial pressure of oxygen (p02) and hydrogen (pH2) as shown in equations (1) and (2) [44]. Table 2. he deinitions of the symbols used in equation 4a and 4b
he atmospheric partial pressure of 0.2095 atm for oxygen and 5×10−5 atm for hydrogen were used to calculate standard electrode potential [44]. At room temperature, pH can be calculated using the Sorensen equation [44],
in which [OH-] is the concentration of hydroxide, OH-, in mole/liter. Table 1 shows the calculated values of pH, Ec0, and Ea0 for KOH concentration used in this experiment.
Table 1. Calculated values of pH, Ec0 and Ea0 at room temperature as a function of KOH concentration he voltammograms from water electrolysis were analyzed using the Butler Volmer equation and Ohm’s law [44]. At high E–Ec0, equation (4) becomes Ohmic term dominant and at low E–Ec0, it becomes Butler Volmer term dominant. herefore, the data can be analyzed separately at 2 diferent regions.
he inconsistency in the aforementioned modiied Butler Volmer it is that it regards the resistive and Butler Volmer term dominant portions as independent identities, whereas in reality, the two factors are related. To address this inconsistency, a new modiied Butler Volmer it was proposed. If the resistance is assumed to be rc, then the voltage drop caused by rc is Ic x rc, where Ic is the total current in the system. his must be subtracted from E to ind the true potential. herefore, the equation 4(a) can be expressed as below without separating it in 2 voltage regions.
hen, to isolate the E – Ec0 term on one side, the exponent must be removed.
*hese equations can substitute Ja for Jc, 1–αa for αc, and Ea0 for Ec0 to produce the formula related to the anode. With this inal equation, it was possible to it the voltammogram curve using one it curve. To ind the desired rc, αc, and Jc0 values, Visual Basic (VB) programming was used, employing a search algorithm to minimize the discrepancy between the measured data and the calculated data from equation 5 within the range of parameters.
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Street Broad Scientific Novel Design in Surface Morphology Many international energy researchers have attempted to make electrode surfaces using nanomaterials. To know the impact of those nanoparticles to surface area, it is necessary to calculate the surface area as a function of particle size. he equation to ind the surface area of the electrode for a square pattern given the distance between two squares (a) and the length of one side of the square (d) with a height (h) is stated thus:
In igure 1, the area in equation (8) is plotted with the height (h) is set to be the smaller of the dimensions d and a, which describes the resolution of the patterning technique, and, therefore, the maximum height that can be attained for the structure. By this deinition, aspect ratio is kept constant, i.e. as pattern size decreases, thickness decreases, which is characteristic of electrode surfaces made using nanoparticles with various particle sizes. Surprisingly, there is no additional advantage in reducing pattern resolution, which is relevant to the particle size. However, if the height of the pattern was constant and lengths of the sides of the structures were reduced (essentially increasing aspect ratio) the surface area increase becomes signiicantly greater, as shown in igure 2. As shown, it is apparent that within a given thickness, smaller and closer-spaced patterns yield greater efective surface areas.
REsEaRch If the aspect ratio is given, the shape of the sidewall can further increase the efective surface area. he underlying goal of this research, then, was to synthesize mushroomtop shaped structures atop electrodes. Figure 3 shows the impact of the sidewall shape to the efective surface area. It is apparent that the mushroom top shape has the greatest impact. his structure can be realized with a relatively simple and economically viable method using photoresist patterning and electroplating.
Figure 3. Schematic diagram of photo resist proile and plated ilm. (a) Pyramid shape structure. Additional area is A-B per side (b) Mushroom top shape structure. Additional area is A+B per side (c) Standard square structure. Additional area is A per side. Figure 4 shows how much additional area can be made by having mushroom top surface morphology. he calculation was made based on assumptions of a ilm thickness of 6 um, a side wall angle of 45 degrees, and a spacing between square patterns of 75 um. With these speciications, the mushroom top surface morphology is shown to produce 13.4% more surface area compared to a straight sidewall with an aspect ratio of 0.06.
Figure 1. Surface area with ixed aspect ratio as function of length and spacing of square patterns. Aspect ratio assumed to be 2.
Figure 2. Surface area with constant thickness as a function of length and spacing between square patterns. hickness was set at 100 um. 8 | 2012-2013 | Volume 2
Figure 4. he calculated additional surface area from square patterns with mushroom top with 45 degree angle and straight sidewall. he thickness of the ilm wasassumed to be 6 um. he spacing between the patterns was ixed at 75 um.
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Experimental Design An alkaline solution of potassium hydroxide (KOH) was used as the electrolyte in the experiment. Platinum was used as a counter electrode and Pt or Ag/AgCl in 4 M KCl were used as reference electrodes, Pt for its high resistance to corrosion, and Ag/AgCl in 4 M KCl for its stable standard electrode potential under various electrolyte concentrations and temperature of the system. Keithley current and voltage measurement devices and HP current/ voltage power supply were connected to the computer via GPIB connection, and the instruments were controlled remotely using VEE (Virtual Engineering Environment) graphic user interface programming language. he electrochemical measurement was cyclic voltammetry, where the independent variables were current or voltage bias and scan speed, and the dependent variables were system voltage for current bias and system current for voltage bias of each half cell. Electroplating System Two types of plating tested were electroless plating and electroplating. he plating solutions were bought from Caswell Plating Inc. and were operated under NCSSM’s lab hood to maintain air circulation. he metal ilm used as a seed layer was sputter coated Ni ilm, on 100 mm diameter glass wafers, with a thickness of 80 nm. Electroplating has two advantages over electroless plating. With electroplating, it becomes simpler to control the ilm thickness because it can be accurately controlled by current density and plating time. Also the electroplating is done at a much lower temperature and is much safer for the photoresist. herefore, electroplating was selected as the ilm deposition method for the experiment, after both methods were tested. Table 3 shows characteristics of electro- and electroless plating.
Figure 5. (a) Electroplating set up w/Keithley 220 power supply, multimeter (voltage measurement), hot plate and thermometer. Wafer holder/anode is visible inside plating solution. (b) Ni sheet metal for anode and Ni ilm on a glass wafer for plating. Several trials were required to ind an optimal electroplating condition. he inal procedure involved 1 cm by 3 cm Ni metal slices which were plated for 30 minutes at 43.3° C with 14 mA of current bias (from the Caswell plating manual) for a plate thickness of 2 um. Figure 6 shows the SEM image of the plated Ni ilms plated at this condition.
Figure 6. SEM image of ~2 um plate thickness with 14 mA applied current for 30 minute plate time.
Table 3. Comparison between Electroplating and Electroless Plating in certain categories. A Ni metal sheet was used as the anode, and a mount was made to hold the anode and Ni wafer in place in the solution, as shown in igure 5. Heat-shrink tube covered springs were used to keep the Ni wafers to be plated stable, and the anode was mounted onto the acrylic base plate using screws.
Photoresist Patterning Procedure Photoresists are nonconductive, photosensitive polymer ilms that can be developed to create patterns on a surface. Typical photoresists are spin-coated onto surfaces and come in two variations: negative ilms and positive ilms. Negative photoresist patterns develop the opposite of the mask that covers it during light exposure; essentially, if there is a dark location on the pattern, the photoresist underneath it will be washed away by the developer solution during developing [45]. Depending on the photoresist material, the contact method during UV exposure, the developing time and the heat treatment condition, the sidewalls of patterned photoresists could generate any of the three types shown earlier in igure 3. While electroplating, then, Ni metal would deposit onto the ilm on conductive surfaces in contact with the solution. he Ni ilm naturally forms to the morphology of the patterned photoresist. Volume 2 | 2012-2013 | 9
Street Broad Scientific he photo mask with 100 – 700 um square patterns were designed using CAD software and laser printed on a acrylic sheet by CAD/Art Services, Inc., a photoresist mask printing service provider. AZ2070 is a negative photoresist that comes in a liquid form. It was spin coated and heat-treated to solidify for exposure and developing. After the photoresist was poured, the wafer was spun at 2000 rpm for 30 seconds on a vacuum chuck, then baked at 100° C for 1 minute to achieve a photoresist thickness of 9 um. For AZ2070, there were three parts to the developing process: exposure to 350-400 nm peak UV light with a patterned mask, heat treatment, then developing with MIF300 to remove the unexposed photoresist and reveal the design. After multiple trials, an optimal developing condition for a positively sloped sidewall was found: 2.5 minute UV exposure, 1.5 minute heat treatment at 120° C, then 1.5 minute developing in MIF300. A cross sectional image of the photoresist on the Ni ilm and the resulting electroplated Ni ilm is shown in igure 7. he additional area from the sidewall shape of the plated Ni ilms shown in SEM cross section was calculated as ~20.1 um per unit side length with 5.1 um thickness as shown in igure 8. After Ni electroplating with the desired conditions, the photoresist was stripped in acetone. Figure 9 shows optical microscope images of plated Ni ilms before and after the photoresist was stripped after plating.
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Figure 9. Optical microscope picture of electroplated Ni ilms. (a) 300 um x 300 um pattern with 300 um spacing with photo resist. (b) 500 um x 500 um pattern with 300 um spacing with photoresist. (c) 500 um x 500 um pattern with 300 um spacing after photoresist stripped in acetone bath.
Figure 10. (a) Voltammogram test set up. HP6632B power supply, Keithley 617, Keithley 192 multimeter and Electrodes are mounted on an acrylic board. (b) Electrode holder. Figure 7. Cross sectional image of (a) photoresist on Ni coated glass substrate with a curved sidewall and (b) electroplated Ni ilm showing the curved sidewall of the mushroom top.
Figure 8. Calculated additional surface area from overhang structure based on photo resist morphology.
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Finding the Optimum KOH Concentration
Figure 11. Voltammogram of water electrolysis with Pt anode, cathode and reference electrode in 0.5, 1.0, 1.5, 2.0 M KOH solutions.
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Table 4. Extracted parameters with 0.5, 1.0, 1.5 and 2 M KOH solutions.
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Figure 13 shows resistance of cathode and anode as a function of KOH concentration. hough the resistance continues to decrease as concentration increases, the change slows down at 1 M KOH and appears to remain similar. Because of this behavior, 1 M KOH solutions were selected for the rest of the experiment.
Figure 13. Resistance of Pt anode and cathode as a function of KOH concentration Figure 12. Example voltammograms of water electrolysis with Pt electrodes at 20째 C for (a) a cathode with 0.5M KOH and (b) an anode with 0.5M KOH. Blue diamonds-measured data, red lines-Ohmic it, green lines-Butler Volmer it. To ind the optimum KOH concentration, 0.5, 1.0, 1.5 and 2 M KOH solutions were tested with Pt as both the anode and cathode. 1 M KOH solution was found to be the optimal solution with a high conductivity using the minimal amount of KOH. he data is shown in igure 11. Figure 12 shows example graphs with measured voltammograms with itted curves by Butler Volmer and Ohmic equations. his is the traditional way of itting the voltammogram in 2 separate voltage regions as described in theory section. It is clear the two it curves show larger discrepancy as they approach the middle region. Table 4 shows the parameters calculated from the analysis using equation (4). From the data in Table 4, it is clear that the limiting resistance decreases as the KOH concentration increases.
Catalytic Efect of Pt versus Ni Electrodes he dependence of electrolysis systems on diferent metals and their inherent catalytic efects, by comparing diferent standard electrode potentials, could be seen by analyzing the voltammograms from a Pt/Pt system versus a Ni/Ni system, as shown in igure 14. he Pt ilm had 2 layers, 25 nm thick Ti adhesion layer at the bottom and 50 nm of Pt on the top, evaporated by an e-beam on a 100 mm diameter glass substrate. he Ni ilm was 80 nm thick, deposited with a sputtering system. For the cathode, the curves in igure 14 look similar; however, the turn-on voltage at the anode has a dramatic diference. With Ni metal, the turn-on voltage became much lower than Pt, which indicated better catalytic efect. For wafer electrolysis, Ni is clearly better than Pt not only because it is cheaper, but also because it has a more pronounced catalytic efect.
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Figure 14. Voltammogram of water electrolysis with Pt ilm (Ti/Pt=25/50 nm thick) and Ni ilm (80 nm thick). Electrode Mounting Method To make it sure to have only the electrode material of interest is in contact with the solution, the electrical connection has to be made outside of the solution. Because of this coniguration, there was always an extra voltage drop from the ilm between the surface of the electrode inside the solution and the place where the contact was made, even though the Kelvin probe method was used to remove the voltage drop from the cables [46,47]. After a few iterations, the inal design for sample mounting for electrolysis was developed, as shown in igure 15. he metal wafers were cut into 1 cm by 3~4 cm slices. Telon tape was used to deine the active area and to separate the active area from contact wire. A small piece of aluminum foil was placed underneath the Telon to reduce the voltage drop between the active area of electrolysis and the electrical contact. For each sample, 2-electrode contacts were made to make a Kelvin probe coniguration, one for power supply and one for voltage sense to minimize the efect of resistance from the wiring.
REsEaRch keep the pins from breaking the sample underneath. For Pt/Ni electrode system, to make more accurate measurement, Ag/AgCl in a 4 M KCl solution was used as reference electrode. [48] he advantage of using Ag/AgCl as a reference electrode was its stabile standard electrode potential. At room temperature its standard electrode potential is known to be at 0.2 V compared to the Pt/H+ standard electrode, and has been found to be stable for various temperatures as well [49]. With the inal design of the electrode mounting scheme, the resolution of the voltammetry setup was conirmed to be high enough to even detect bubble formation during electrolysis. With very narrow electrode opening in working electrode, dense gas formation is expected. At high current, the gas will form big size bubbles and it can block the part of electrode surface. As the electrode is partially covered by a bubble, the resistance of the system increases and it will be detected as current decrease if the system is sensitive enough. Figure 16 shows the voltammogram with Ni ilm with 0.087 cm2 opening. At high current region, the current oscillation is visible and it correlates to the period of bubble formation. Figure 17 shows a typical picture of the bubble when it is the biggest and right after it is removed from the surface because of its buoyancy.
Figure 16. he voltammogram of Ni electrode with 0.087 cm2 opening.
Figure 15. Schematic diagram of sample loading and electrical connection set up for water electrolysis for Ni ilms. he contact was made using gold-plated pins soldered to a wire on a circuit board connected to the acrylic back plate with screws with springs around them to apply force against the screws to preserve the lifetime of the pins and 12 | 2012-2013 | Volume 2
Figure 17. he pictures of Ni electrode with 0.087 cm2 opening when (a) the bubble is biggest so a part of the surface is covered and (b) the bubble is loated up because of its buoyancy.
REsEaRch Impact of Patterned Plated Ni Electrode with Mushroom Top Morphology To ind the impact on electrolysis eiciency of the new surface morphology, the data from the sputtered Ni ilms with 2 diferent opening areas have been tested, as well as 3 diferent designs of patterned, plated Ni ilms with mushroom top surface morphologies. To keep the plated Ni ilm thickness constant for all 3 samples, the plating was done on a half of 100 mm diameter wafer with 3 different photo resist patterns. he wafer was cut after the plating is done using diamond saw. Also to improve the accuracy of the data analysis, a modiied Butler Volmer equation (equation 5) was used. Figure 18 shows an example of the analysis on sputtered Ni ilm with 0.87 cm2 opening with both the conventional method (equation 4) and the new method developed in this research (equation 5). To use equation (5), a Visual Basic program was made to ind the parameters with the minimum square error between the measured data and the model. From the graph, it was clear that the new analysis method was more accurate, not only in the medium voltage region but also in the low voltage region. Figure 19 shows the current voltage characteristics of 5 samples tested, sputtered Ni ilms with 0.087 cm2 and 0.87 cm2 opening area and 3 electrodes with patterned plated Ni with mushroom top surface morphology. he pattern structures are 300x300 um2 with 100 um spacing, 300x300 um2 with 300 um spacing and 500x500 um2 with 100 um spacing. Since the data from a 0.087 cm2 opening was oscillating in the high current region, only the maximum point of each oscillation period was taken for analysis because those data points represent when there is no bubble covering the electrode surface.
Figure 18. Comparison between experimental data (diamonds), original mod-BV it (red –low voltage region), Ohmic it (green – high voltage region), and new modBV it (blue circles) using the search algorithm developed in Visual Basic.
Street Broad Scientific From igure 19, it is clear that there is lot more current lowing at a given voltage if the opening area is larger. he plated ilms with mushroom top surface morphology shows largest current but more rigorous Butler Volmer analysis is necessary to see exactly how much impact was made by this surface modiication.
Figure 19. Current Voltage characteristics of 5 diferent Ni ilm electrodes. ▬ sputtered Ni ilm with 0.087 cm2 opening. It shows oscillating current as a result of bubble accumulation. ▬ maximum data points from each oscillation period from sputtered Ni ilm with 0.087 cm2 opening. ▬ sputtered Ni ilm with 0.87 cm2 opening. And patterned plated Ni ilm with mushroom top morphology with the patterns of 500x500 um2 with 100 um spacing(▬), 300x300 um2 with 100 um spacing(▬) and 300x300 um2 with 300 um spacing(▬). he highest current was expected from 300x300 um2 with 100 um spacing. However, appeared that 500x500 um2 with 100 um spacing showed the highest current. It is still under investigation but it seems that the patterns generated were more rounded rather than square, and this distortion becomes more pronounced as the pattern size and spacing decreased due to low resolution of the photolithography setup used in this experiment. Figure 20 shows the it parameters from the new model (equation 5) found with the aforementioned VB program. With the new it method, three graphs were generated, one for each critical parameter as a function of surface area for the anode and cathode. In igure 20a and 20d, there is a clear trend of resistance reduction as area increases. With a linear it of sputtered Ni ilms with 2 diferent opening areas, it is noticeable that the resistances of patterned plated samples show lower resistance than expected from the linear extrapolation. he resistance plot as a function of area with efective surface area of mushroom top morphology (green triangles in igure 20a and 20d) its very well with this linear trend.
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REsEaRch of the system decreased below the expected resistance from the linear extrapolation of the sputtered Ni ilms with known opening areas. From SEM pictures of the mushroom top patterns, the estimated advantage of the structure was about 4 times more than straight sidewall. With linear approximation, the average reduction in resistance was 20.4% more than what is expected from linear approximation, which translates into a 25.6% increase in eiciency.
Future Works
Figure 20. (a, b, c for anode; d, e, f for cathode) Resistance, Exchange Current Density ( Jc0, Ja0), and Charge Transfer Coeicients (1-Îąa, Îąc) compared to changes in Surface Area. Blue diamond represents data from the sputtered Ni ilms, while the red squares are from the patterned plated samples without consideration of additional surface area of the plated Ni patterns. For resistance plots (a and d), the same resistance data was plotted as a function of surface area with consideration of the sidewall shape of the plated Ni ilm as green triangles.
Conclusion his research demonstrated the impact of novel electrode surface design with mushroom top morphology to increase the efective surface area by using simple photolithography and electroplating. his design can be applied to other patterning methods with various aspect ratios. With cyclic voltammetry, these metal ilms were tested to gather voltammograms. Using a conventional and newly proposed modiied Butler Volmer equation with limiting resistance, critical parameters were extracted and analyzed in relation to the change in surface area. he resistance 14 | 2012-2013 | Volume 2
In the future, the experiment should be done to ind the trend of electrolysis eiciency as a function of pattern size and spacing. To do so, photolithography techniques with higher controllability is required. Also it is desirable to use smaller pattern size to increase additional surface area from the plated metal sidewall so the efective surface area of this new structure can be further increased. As investigated in this study, higher aspect ratios show a greater efective surface area increase. If we apply this mushroom top surface morphology structures in high aspect ratio materials such as nanowires and nanotubes, it will further increase the eiciency. Another focus of future works can be on other nonnoble metals that may provide greater catalytic efect than Ni, including cobalt or manganese, which have been previously tested and characterized in electrolytic systems to be suitable replacements for noble metals. With consideration to the future, high eiciency electrolysis will very likely become one of the greatest sources of fuel for the future.
Acknowledgements I’d like to acknowledge my mentor, Dr. Lee, for reading materials and lab assistance he provided me throughout the project, as well as the motivation he had to try a new idea out of his ield. Also, my mother and sisters who cheered me on before going to bed, whose spirits kept me going till 4. Special thanks to the NCSSM Research in Chemistry program and Dr. Halpin for providing the bulk of the funding for my research and the resources to have my materials accepted and presented at many conventions throughout the year.
REsEaRch References [1] Henry Hub Gulf Coast Natural Gas Spot Price, http:// www.eia.gov/dnav/ng/hist/rngwhhdm.htm, last visited 09/27/2012. [2] M. I. Godinho, M. A. Catarino, M. I. da S. Pereira, M. H. Mendonca, and F. M. Costa. Efect of partial replacement of Fe by Ni and/or mn on the electrocatalytic activity for oxygen evolution of the CoFe2O4 spinel oxide electrode. Electrochemica Acta, 47:4307-4314, 2002. [3] C. C. Hu and Y. R. Wu, “Bipolar performance of the electroplated iron-Ni deposits for water electrolysis.”, Materials Chemistry and Physics, 82, pp588-596, 2003. [4] J. Ponce, J. L. Rehspringer, G Poillerat, and J. L. Gautier,” Electrochemical study of Ni-aluminum-manganese spinel NixAl1-xMn2O4. Electrocatalytical properties for the oxygen evolution reaction and oxygen reduction reaction in alkaline media”, Electro-chemica Act, 46:3373-3380, 2001. [5] Chebotareva, Natalia and Nyokong, Tebello. “First-row transition metal phthalocyanines as catalysts for water electrolysis: a comparative study” Electrochimica Acta, 42:3519 – 3524, 1997. [6] V. Rashkova, S Kitova, I. Konstantinov, and T. Vitanov.” Vacuum evaporated thin ilms of mixed cobalt and Ni oxides as electrocatalysts for oxygen evolution and reduction.”Electrochemica Act, 47:1555-1560, 2002. [7] R. N. Singh, N. K. Singh, and J. P. Singh. “Electrocatalytic properties of new active ternary ferrite lm anodes for O2 evolution in alkaline medium”. Electrochemica Acta, 47, 3873-3879, 2002. [8] F. I. Mattos-Costa, P. de Lima-Neto, “S. A. S. Machado, and L. A. Avaca. Characterization of surfaces modiied by sol-gel derived RuxIr1- xO2 coatings for oxygen evolution in acidic medium.” Electrochemica Acta, 44:1515{1523, 1998}. [9] G. L. Elizarova, G. M. Zhidomirov, and V. N. Parmon. Hydroxides of transition metals as artiicial catalysts for oxidation of water to dioxygen. Catalysis Today, 58:71{88, 2000. [10] C.-T. Hsieh, W.-Y. Chen, I.-L. Chen, and A. K. Roy, “Deposition and activity stability of Pt–Co catalysts on carbon nanotube-based electrodes prepared by microwaveassisted synthesis,” Journal of Power Sources, vol. 199, pp. 94–102, Feb. 2012. [11] Z. Hu, D. M. Zhou, R. Greenberg, and T. hundat, “Nanopowder molding method for creating implantable high-aspect-ratio electrodes on thin lexible substrates.,” Biomaterials, vol. 27, no. 9, pp. 2009–17, Mar. 2006. [12] C.-J. Huang, P.-H. Chiu, Y.-H. Wang, W.-R. Chen, T.H. Meen, and C.-F. Yang, “Preparation and characterization of gold nanodumbbells,” Nanotechnology, vol. 17, no. 21, pp. 5355–5362, Nov. 2006. [13] Y. Lei, W. Cai, and G. Wilde, “Highly ordered nanostructures with tunable size, shape and properties: A new way to surface nano-patterning using ultra-thin alumina masks,” Progress in Materials Science, vol. 52, no. 4, pp. 465–539, May 2007.
Street Broad Scientific [14] S.-C. Lin, Y.-F. Chiu, P.-W. Wu, Y.-F. Hsieh, and C.-Y. Wu, “Templated fabrication of nanostructured Ni brush for hydrogen evolution reaction,” Journal of Materials Research, vol. 25, no. 10, pp. 2001–2007, Jan. 2011. [15] C. Microanalytics, L. Maltings, and P. Row, “Nanoelectrodes , nanoelectrode arrays and their applications,” pp. 1157–1165, 2004. [16] T. N. Nanowires and S. H. Magnetization, “Nanotubes to Electrodes.” [17] L. F. Petrik, Z. G. Godongwana, and E. I. Iwuoha, “Platinum nanophase electro catalysts and composite electrodes for hydrogen production,” Journal of Power Sources, vol. 185, no. 2, pp. 838–845, Dec. 2008. [18] M.-S. Wu and P.-C. J. Chiang, “Electrochemically deposited nanowires of manganese oxide as an anode material for lithium-ion batteries,” Electrochemistry Communications, vol. 8, no. 3, pp. 383–388, Mar. 2006. [19] Ghosh, S.K; Grover, A.K; Dey, G.K;Totlani, M.K, “Nanocrystalline Ni–Cu alloy plating by pulse electrolysis” Surface & Coatings Technology (0257-8972), 2000, Volume 126, Issue 1, pp. 48 - 63. [20] Ranganathan, David; Zamponi, Silvia;Berrettoni, Mario; Layla Mehdi, B; Cox, James A; “Oxidation and low-injection amperometric determination of 5-hydroxytryptophan at an electrode modiied by electrochemically assisted deposition of a sol-gel ilm with templated nanoscale pores” Talanta (0039-9140), 09/2010, Volume82, Issue 4, pp. 1149 - 1155. [21] Shibli, S M. A and Dilimon, V S. “Development of nano IrO 2 composite-reinforced nickel–phosphorous electrodes for hydrogen evolution reaction” Journal of Solid State Electrochemistry(1432-8488), 08/2007, Volume 11, Issue8, pp. 1119 - 1126. [22] Gao, Feng; Yang, Yifu; Liu, Jun; Shao, Huixia. “Method for preparing a novel type of Pt–carbon iber disk ultramicroelectrode” Ionics (0947-7047), 02/2010, Volume 16,Issue 1, pp. 45 - 50. [23] Brown, I.J and Sotiropoulos, S. “Preparation and characterization of microporous Ni coatings as hydrogen evolving cathodes” Journal of Applied Electrochemistry(0021-891X), 01/2000, Volume 30, Issue1, pp. 107 - 111. [24] Sanchez, Pablo Lozano and Elliott, Joanne M. “Underpotential deposition and anodic stripping voltammetry at mesoporous microelectrodes” he Analyst (0003-2654), 05/2005, Volume 130, Issue 5, p. 715. [25] Łosiewicz, Bożena. “Experimental design in the electrodeposition process of porous composite Ni–P+TiO2 coatings” Materials Chemistry and Physics (0254-0584), 08/2011, Volume 128, Issue 3, pp. 442 - 448. [26] Nikolić, Nebojša D; Branković, Goran;Popov, Konstantin I. “Optimization of electrolytic process of formation of open and porous copper electrodes by the pulsating current (PC) regime” Materials Chemistry and Physics (0254-0584), 2011, Volume 125, Issue 3, pp. 587 - 594. [27] Chen, Shun-Tong and Luo, Tsu-Sheng. “Fabrication of micro-hole arrays using precision illed wax metal deposition” Volume 2 | 2012-2013 | 15
Street Broad Scientific Journal of Materials Processing Tech(0924-0136), 2010, Volume 210, Issue 3, pp. 504 - 509. [28] Tang, Shaochun; Tang, Yuefeng; Gao, Feng; Liu, Zhiguo; Meng, Xiangkang. “Ultrasonic electrodeposition of silver nanoparticles on dielectric silica spheres” Nanotechnology (0957-4484), 07/2007,Volume 18, Issue 29, p. 295607. [29] Brown, I.J; Clift, D; Sotiropoulos, S. “Preparation of microporous nickel electrodeposits using a polymer matrix” Materials Research Bulletin (0025-5408), 1999, Volume 34, Issue 7, pp. 1055 - 1064. [30] Sotiropoulos, S; Brown, I.J; Akay, G;Lester, E. “Nickel incorporation into a hollow ibre microporous polymer: a preparation route for novel high surface area nickel structures” Materials Letters (0167-577X), 1998,Volume 35, Issue 5, pp. 383 - 391. [31] El-Sherik, A.M; Erb, U; Page, J. “Microstructural evolution in pulse plated nickel electrodeposits” Surface & Coatings Technology (0257-8972), 1997, Volume 88, Issue 1, pp. 70 - 78. [32] Mukai, Kohki; Kitayama, Shinya;Kawajiri, Yasunobu; Maruo, Shoji. “Micromolding for three-dimensional metal microstructures using stereolithography of photopolymerized resin” Microelectronic Engineering (0167-9317), 2009, Volume 86, Issue 4, pp. 1169 - 1172. [33] Walsh, F.C; Ponce de León, C; Kerr, C; Court, S; Barker, B.D. “Electrochemical characterisation of the porosity and corrosion resistance of electrochemically deposited metal coatings” Surface & Coatings Technology (0257-8972), 2008, Volume 202, Issue 21, pp. 5092 - 5102. [34] Wang, Jian; Wei, Liangming; Zhang, Liying; Zhang, Yafei; Jiang, Chuanhai. “Electrolytic approach towards the controllable synthesis of symmetric, hierarchical, and highly ordered nickel dendritic crystals” CrystEngComm (14668033), 02/2012,Volume 14, Issue 5, pp. 1629 - 1636 [35] Sode, A; Ingle, N.J.C; McCormick, M;Bizzotto, D; Gyenge, E; Ye, et. al. “Controlling the deposition of Pt nanoparticles within the surface region of Naion” Journal of Membrane Science (0376-7388), 2011, Volume 376, Issue 1, pp. 162 - 169. [36] Mohanty, U S. “Electrodeposition: a versatile and inexpensive tool for the synthesis of nanoparticles, nanorods, nanowires, and nanoclusters of metals” Journal of Applied Electrochemistry(0021-891X), 03/2011, Volume 41, Issue3, pp. 257 - 270. [37] Domínguez-Crespo, M.A; Ramírez-Meneses, E; Torres-Huerta, A.M; Garibay-Febles, V; Philippot, K. “Kinetics of hydrogen evolution reaction on stabilized Ni, Pt and Ni–Pt nanoparticles obtained by an organometallic approach” International Journal of Hydrogen Energy(0360-3199), 03/2012, Volume 37, Issue6, pp. 4798 - 4811. [38] R.K. Shervedani and A. Lasia. “Studies of the hydrogen evolution reaction on Ni–P electrodes” J. Electrochem. Soc. 144, 511 (1997). [39] D.R. Kim, K.W. Cho, Y.I. Choi, and C.J. Park. “Fabrication of porous Co–Ni–P catalysts by electrodeposition and 16 | 2012-2013 | Volume 2
REsEaRch their catalytic characteristics for the generation of hydrogen from an alkaline NaBH4 solution.” Int. J. Hydrogen Energy 34, 2622 (2009). [40] S.I. Tanaka, N. Hirose, and T. Tanaki. “Evaluation of raney-nickel cathodes prepared with aluminum powder and titanium hydride powder” J. Electrochem. Soc. 146, 2477 (1999). [41] Seonyul Kim, Nikhil Koratkar, Tansel Karabacak, and Tob-bi-Ming Lu, Applied Physics Letters, 26, 263106, 2006. [42] Ibrahim M. Sadiek, Ahmad M. Mohammad, Mohamed E. El-Shakre, M. Ismail Awad, Mohamed S. El-Deab, and Bahgat E. El-Anadouli, “ Electrocatalytic Evolution of Oxygen Gas at Cobalt Oxide Nanoparticles Modiied Electrodes, Int. J. Electrochem. Sci, 7 (2012) 3350 – 3361. [43] Robert B. Dopp, “Hydrogen Generation via water electrolysis using highly eicient nanometal electrode”, announced in a website, http://www.qsinano.com, last visited 9/27/12. [44] Matthew D. Merill, “Water Electrolysis at thermodynamic limit”, Ph. D. hesis, Florida State Univ., 2007, http:// etd.lib.fsu.edu/theses/available/etd-09092007-185842/unrestricted/MerrillMFall2007.pdf, last visited at 9/1/2012. [45] Debmalya Roy, P. K. Basu and S. V. Eswaran, “Photoresists for microlithography”, Resonance, Vol. 7 Num. 7 , 44-53, July 2012. [46] Andrew P. Schuetze, Wayne Lewis, Chris Brown, and Wilhelmus J. Geerts, “A laboratory on the four-point probe technique”, American Journal of Physics, Volume 72, Issue 2, 149, 2004 [47] S. P. S. Badwal, F. T. Ciacchi and D. V. Ho, “A fully automated four-probe d.c. conductivity technique for investigating solid electrolytes”, Journal of Applied Electrochemistry, Vol.21:721-728 (1991) [48] Gaston A East and M A del Valle, “Easy-to-make Ag/ AgCl reference electrode” Journal of Chemical Education, Volume 77, Issue 1, p. 97 (2000) [49] Maksimov, Igor; Ohata, Masaki; Asakai, Toshiaki; Suzuki, Toshihiro; Miura, Tsutomu; Hioki, Akiharu; Chiba, Koichi, “Temporal stability of standard potentials of silver– silver chloride reference electrodes” Accreditation and Quality Assurance, Volume 17, Issue 5, pp. 529 – 533 (2012)
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Multilevel Distance Labeling - A Wireless Network Problem Tian-Shun Allan Jiang ABSTRACT Multilevel distance labeling is a graph-theoretical solution to the problem of frequency assignment on wireless networks. An optimal labeling reduces the range of radio frequencies assigned to radio stations and eliminates network interference. Due to the ubiquity of wireless networks, a more efective frequency assignment is an important area of study to increase eiciency and quality of communication. We model the problem by representing broadcasting stations as vertices on a graph. A radio labeling of a connected graph G is a mapping F:V(G)→{0,1,2,…} such that |F(u)-F(v)|+d(u,v)≥diam(G)+1 for each pair of distinct vertices u,v∈V(G) where diam(G) is the diameter of G and d(u,v) is the distance between u and v. he span of F, denoted span(F), is deined max(u,v ∈ V(G)) |F(u)F(v)|. hen the radio number of G is denoted In this paper, we introduce a general method to compute the lower bound for rn(G), introduce a method to characterize solutions F on G, and prove a closed-form formula of rn(G) for the path and triangle lollipop graphs.
Introduction Background Wireless communication pervades modern society. Wireless internet, mobile phones, radio, and GPS are just a few of the common applications of wireless technology. An eicient and reliable wireless network must overcome a number of technical challenges; among these is the allocation of broadcast frequencies to minimize interference. An efective method of frequency coordination, a regulatory process for the mitigation of frequency interference, increases the eiciency of wireless communication. In this paper, we focus on the problem of frequency coordination in cellular networks. Cellular systems are designed to minimize both interference and range of channel assignment through frequency reuse. In this system, the coverage area is partitioned into many cells with assigned frequencies. Since signal power is efective within a certain radius from the transmitter, reuse of similar frequency spectra becomes possible at certain distances [4]. his reuse allows cellular system designers to minimize the frequency range used for the whole system. he distance among cells that use similar frequency spectra should be minimized to increase spectral eiciency. However, if the distance is too small, users will receive frequencies from both channels, causing intercell interference [4]. hus, a balance between spectral eficiency and inter-cell interference should be achieved. In this paper, we present a graph-theoretical model of a solution which eliminates inter-cell interference while maximizing spectral eiciency.
Multilevel Distance Labelling We represent cellular stations with vertices on a graph G, and draw edges between vertices if the stations are geographically close. Interference among stations can occur at multiple levels, ranging from the interference between the closest stations with distance one, to the furthest stations, with distance diam(G). Given a connected graph G, for two vertices u,v∈V(G) let d(u,v) be the distance between u and v. hen a radio labeling of G is a function F: V(G)→{1,2,3,…} such that for u,v∈V(G):
he span of F is, span = max(u,v ∈ V(G)) |F(u)-F(v)|. he radio number of G, denoted rn}(G):=min(span(F)). he solutions of G are all labelings F such that span(F)=rn(G). In past work on the problem [1], the usual method has been to ind an upper bound of rn(G) equal to the lower bound of rn(G). However, neither the upper nor lower bounds are easy to establish. his paper addresses some of the challenges and insights when searching for rn(G). In Section 2, some preliminary investigations of the distance labeling problem are presented, and in Section 3, we establish a general methodology to ind the lower bound of a graph G. In Sections 5 and 6, we focus on inding the upper and lower bounds of the radio numbers of two speciic classes of graphs: path and lollipop. his inds and proves the closed form expression of rn(G) for these two graph types. In Section 7, we introduce tightness graphs as a way to classify solutions and in Section 8, several areas of further research are discussed.
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Street Broad Scientific Preliminaries Deinitions 1. Wiggle Room: For two vertices x and y, deine the wiggle room wr(x,y)=|F(x)-F(y)|+d(x,y)-(D+1). Notice that in a valid distance labeling, the wiggle room is nonnegative. 2. Tight: Two vertices x and y are called tight if wr(x,y)=0. 3. Tightness Graphs: he tightness graph GT of a labeled graph G has vertex set V(GT)=V(G) and edge set E(GT )={(x,y)| x,y ∈V(G), wr(x,y)=0}. his idea is further explored in Section 7. 4. Hopping: We deine a hopping, or hopping sequence H(G) to be a permutation of V(G) such that H(G)={h1, h2,…,hn} and F(hi) < F(h(i+1)) for 1 ≤ i ≤ n-1. 5. Tight Hopping: A special case of hopping is where wr(hi, hi+1) = 0 for all 1 ≤ i ≤ n-1. hese are referred to as tight hoppings. Terminology 1. A graph G is deined as G=(V(G),E(G)), where V(G) is the vertex set of G, E(G) is edge set of G, and an edge e∈E(G) is a subset of two vertices v∈V(G). 2. he distance d(x,y) for x,y∈V(G) is the length of a shortest path between x and y. 3. he diameter diam(G) or D of a graph is the maximum distance between two vertices v∈V(G). 4. he path graph Pn is a graph with vertices V(Pn)={v1, v2,…, vn} and edges E(Pn)={(v1, v2),(v2, v3),…, (v(n-1), vn)}.
REsEaRch Observations 1. Do Not Repeat Labels: Claim: No two vertices can have the same labeling. Proof by Contradiction: Assume that there exist two vertices x,y such that F(x)=F(y). By deinition, d(x,y)+|F(x)F(y)| > D and d(x,y) > D. However, this is a contradiction, as d(x,y) ≤ D. 2. An Obvious Upper Bound: Claim: rn(G)≤(n-1)∙D. Proof by Construction: Let our labeling F:V(G)→{0, D, 2D,…,(n-1)D}. his is a distance labeling, because |F(x)F(y)| ≥ D and d(x,y) ≥ 1, which complies with the distance labeling deinition that d(x,y)+|F(x)-F(y)| ≥ D+1. 3. he Inverse Solution: Claim: Given a solution F of G, there exists a corresponding solution F’ of G. Proof by Construction: Let F’(vi)=rn(G)-F(vi). he assignment F’ is also valid, as d(x,y)+|(rn(G)-F(x))(rn(G)-F(y))|=d(x,y)+|F(x)-F(y)|>diam(G). Furthermore, we have span(F)=span(F’). We call this equivalent solution F’ the inverse solution of F. Below is an example of an inverse solutions S1 and S2.
Figure 2.2.1. Inverse Solutions on P5 Figure 2.1.1. Example Path Graph 5. A triangle lollipop graph TLn is a graph with vertices V(TLn)=V(Pn) and edges E(TLn)=E(Pn)U(vn,v(n-2)). For sake of convenience, let us call vn the “lollipopped” vertex.
Figure 2.1.2. Example Triangle Lollipop Graph
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4. A Flawed Labeling: In initial investigations of multidistance labeling on Pn, the following labeling algorithm was conceived: Let us deine a permutation P of V(G) such that
and Pi is the ith element in the permutation. Let F(P0)=0. hen, for all i, label F(P(i+1)) such that wr(F(Pi), F(P(i+1)))=0 (see Deinition 2.1.1), and F(Pi)<F(P(i+1)) for 1 ≤ i ≤ [(n+1)/2], and F(Pi) > F(P(i+1)) for [(n+1)/2] ≤ i ≤ n. (S1 in Figure 2.2.1 is an example of this algorithm.)
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REsEaRch It is easily veriied that this labeling is valid. We also see that span(F)=F(v([(n+1)/2]+1) ). After some calculation, we see that:
Although this algorithm gives rn(G) for all paths with at most 6 vertices, it no longer works for 7 or more vertices, as it was disproven by the computer (see Appendix B problems.). However, this labeling is insightful in that it recognizes that rn(G) grows approximately as (n2), indicating that rn(G) is likely a quadratic function. With these observations and deinitions, we have some interesting tools to approach the distance labeling problem. Of particular interest is the notion of tight hopping. Some experimentation will reveal that not all tight hopping sequences lead to valid distance labelings. Below, we establish a lemma that places proper restrictions on hopping to ensure that it generates a valid labeling for the case of a path graph Pn and triangle lollipop graph TLn .
Distance Labeling on Paths and Triangle Lollipops
We prove that this rule will make the tight hopping on a path a valid distance labeling, by showing that this labeling satisies the deinition |F(u) - F(v)| ≥ diam(G) - d(u,v) + 1 for any two vertices u and v. First, we note that any two vertices hi and hj with |i-j|=1 will satisfy the relationship, as wr(hi, hj)=0. Further, any two vertices hi and hj with |i-j|=2 satisfy the deinition. Let d(hi, h(i+1))=d1 and d(h(i+1), h(i+2))=d2. Now, we may express d(hi, h(i+2))=d3 in terms of d1 and d2. First, we establish the value of h(i+2) compared to hi.
From these equations we get F(h(i+2))-F(hi)=2D+2-d1d2. Now there are two cases: Case 1: d3 = d1 + d2
In this section, we establish some rules and restrictions on tight hoppings for Pn and TLn to ensure that the resultant labeling is a valid distance labeling. Tight Hopping Rules on Paths and Lollipops he following restrictions are necessary and suicient to ensure that a tight hopping on a path or lollipop is a valid distance labeling: 1. If our hopping sequence contains hi→h(i+1)→h(i+2), such that d(hi, h(i+1)) > d(h(i+1), h(i+2)) and d(xi, x(i+2))=|d(hi, h(i+1))-d(h(i+1), h(i+2))|, then d(h(i+1), h(i+2)) ≤ (D+1)/2. 2. If our hopping sequence contains hi→h(i+1)→h(i+2), such that d(hi, h(i+1)) < d(h(i+1), h(i+2)) and d(xi, x(i+2))=|d(hi, h(i+1))-d(h(i+1), h(i+2))|, then d(hi, h(i+1)) ≤ (D+1)/2. Notice that if our hopping sequence is hi→h(i+1)→h(i+2), with d(xi, x(i+2))=d(hi, h(i+1))+d(h(i+1), h(i+2))|, then min(d(hi,h(i+1)), d(h(i+1), h(i+2))) ≤ (D+1)/2 - as we cannot partition a path of length D+1 into two parts with length greater than (D+1)/2. With this, we notice that the restrictions placed on tight hoppings are equivalent to the following restriction:
Clearly, if two hops are in the same direction, we have d3=d1+d2. hen, by the distance labeling deinition, F(h(i+2))-F(hi)≥D+1-d3. Since we already know F(h(i+2))-F(hi)=2D+2-d1-d2, we have
Since D+1>0, when hopping twice on a path in the same direction, there are no restrictions on the values of d1 and d2, other than d1+d2 ≤ D+1 → min(d1, d2) ≤ (D+1)/2 as desired. Case 2: d3 = |d1+d2 | In this case, the two hops are in opposite directions. Without loss of generality, we may let d1>d2. hen, by deinition we have, F(h(i+2))-F(hi) ≥ D+1-d3. Again, since F(h(i+2))F(hi)=2D+2-d1-d2, we have
Since we let d1>d2., we have D+1 ≥ 2d2→(D+1)/2 ≥ d2 as desired. For all cases of vertices hi and hj with i-j≥3, we prove that Volume 2 | 2012-2013 | 19
Street Broad Scientific our restrictions on tight hopping satisfy the deinition through induction. We already have two base cases: i-j=1 and i-j=2 from above. Now, assume that all pairs of vertices hi, hj with i-j ≤ k have wr(hi, hj) ≥ 0. hen, if we consider the hopping sequence hi→h(i+1)→∙∙∙→h(i+k)→h(i+(k+1)), we see that wr(hi, h(i+k)) ≥ 0, and wr(h(i+k), h(i+k+1)) ≥ 0. hus, the vertices hi, h(i+k), h(i+(k+1)) satisfy our above restriction on hopping. hen, we have wr(hi,h(i+(k+1))) ≥ 0. As this proves our induction step, it follows that the a tight hopping with the restriction d(hi,h(i+1)) ≤ (D+1)/2 generates a valid distance labeling on Pn Verifying Path Labelings Above, we showed that any tight hopping on a path or lollipop satisfying:
REsEaRch
Since rn(G) ≥ min(F(xn)), if we can maximize ∑(i=1)(n-1) di on a graph G, we will have a lower bound for rn(G). We call ∑(i=1)(n-1)di the total hopping distance, as it is the sum of all the distances as we hop over a sequence of the n vertices of G. hen we see that our lower bound makes sense, as increasing the total hopping distance decreases the amount we must increment the labelings. his total hopping distance can be maximized for the path graph, and the triangle lollipop graph. However, for a general graph G, inding the maximum total hopping distance may be NP-complete due to a reduction from L(2,1) labelings [3].
Proof for Paths will be a valid distance labeling. hus, one way to show that a given labeling F is indeed a valid distance labeling on Pn or TLn, is by showing that: 1. he labeling is a tight hopping, and 2. For 1 ≤ i ≤ n-2, we have min(d(hi, h(i+1)), d(h(i+1), h(i+2))) ≤ n/2.
here are certain classes of graphs for which we may compute the lower bound and construct an upper bound matching the lower bound. his ability to compute the lower bound is related to the ability to ind the maximum hopping distance. It follows that paths are special cases when trying to ind the maximum hopping distance, as distances are found by subtracting vertex numbers.
he Lower Bound
heorem: For any n ≥ 4
We describe a method to establish a lower bound for graph G. Total Hopping Distance Let us consider the vertices of G in order of increasing label. hen, if G has vertices V(G)={v1,v2,…,vn}, let {x1, x2,…, xn}, be a permutation of V(G) such that F(x(i+1))>F(xi) for all 1 ≤ i ≤ n-1.
We prove the result by sandwiching the value of rn(G) between a coinciding upper bound and lower bound.
For convenience, let F(x(i+1))-F(xi)=fi and d(x(i+1), xi) = di.
Lower Bound From Section 3, we know that F(xn) ≥ (n-1)(D+1)-∑(i=1) (n-1)di It happens that the maximum hopping distance is diferent for even length and odd length paths.
By deinition we have F(x1) ≥ 0 and fi ≥ D+1-di. We also deine the contribution of a vertex cb(xi)=fi+di-D-1. We see that fi is minimized when cb(xi)=0 for 1 ≤ i ≤ n-1.
Odd Length Paths Note: if we have ∑(i=1)((2k+1)-1)di ≤ 2k2+2k-2 then we are done, as:
Now, we note that the maximum labeled vertex F(xn)=∑(i=1)(n-1)fi . here exists an assignment of di’s such that F is a valid distance labeling, so we have
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Claim: if ∑(i=1)2kdi > 2k2+2k-2 then ∑(i=1)^2kdi =2k2+2k-1, and there exists a vertex vi such that wr(vi)=1
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REsEaRch Proof of Claim: Since each d(xi, x(i+1))=|j-j’| if xi = vj and x(i+1)=vj’, ∑(i=1)2kdi is the sum of 4k j’s where half of the j’s are positive, the other half are negative, and 1 ≤ j ≤ 2k+1. Furthermore, 2k-1 terms appear twice, and 2 terms appear once. (he terms appearing once represent the min and max labeled vertex).
In Case 2, we have v(k+1)=x1 and v(k+2)=x(2k+1). Like the previous case, we know that if xi ≥ k+1, then x(i+1) ≤ k. Now, consider xi=2k+1. hen, x(i-1) ≤ k and x(i+1) ≤ k. his again contradicts our hopping criterion above. As both distances di-1, di ≥ (D+1)/2, we know that either cb(x(i-1))=1 or cb(x(i+1))=1, which forces our maximum value to increment by at least 1. By taking the sum of all inequalities and accounting for the contribution of 1, we have
and we have shown the lower bound for odd paths. Figure 5.1.1. Assigning values of j on P5 hen, to maximize ∑(i=1)2kdi, we need to minimize the absolute values of the negative terms and maximize the values of the positive terms. here are two cases achieving this maximum summation: Case 1: We have positive values of j belonging to {k+2, k+3,…, 2k+1}, each of which appears twice (note that this is 2k terms), negative values of j belonging to {1, 2,…, k-1}, each of which appears twice, and negative values for k and k+1 both appearing once. Case 2: We have positive values of j belonging to {k+3, k+4,…, 2k+1}, }, each of which appears twice, positive values for k+1 and k+2, and negative values of j belonging to {1, 2,…, k}, each of which appears twice. In both cases, we get:
In Case 1, we have v(k+1)=x1 and vk=x(2k+1). (It does not matter if the positions of x1 and x(2k+1) are switched due to the inverse solution). Since each di is composed of a positive component and a negative component, we know that if xi ≥ k+2 (in part B below), then x(i+1) ≤ k+1.
Even Length Paths Finding this lower bound is simpler than for odd length paths, as there is only one way to maximize the hopping distance. his is due to the even split between positive and negative terms of j. Claim: ∑(i=1)(2k-1)di ≤ 2k2-1 Proof of Claim: Using the above logic, we know that we have 4k-2 terms of 1 ≤ j ≤ 2k, 2k-2 of which occur twice and 2 of which occur once. Again, half of these terms are positive and the other half are negative. he maximization of this sum occurs when we have positive values of j belonging to {k+2, k+3,…, 2k}, each of which appears twice, positive values for k and k+1 each appearing once, and negative values of j belonging to {1, 2,…, k-1}, each of which appears twice. From this, we get:
If follows that:
as desired [1]. Upper Bound Odd Length Paths Now, consider xi=1. hen, x(i-1) ≥ k+2 and x(i+1) ≥ k+2 . However, this contradicts our tight hopping criterion from Section 3.1. hus, since both distances di-1, di ≥ n/2, we know that either cb(x(i-1))=1 or cb(x(i+1))=1, which forces our maximum value to increment by at least 1. Volume 2 | 2012-2013 | 21
Street Broad Scientific We label the vertices of G as follows:
REsEaRch hen we get x(2k+1)=2k(2k+1)-∑(i=1)2kdi , and the problem is reduced to inding the hopping distance of this speciic labeling scheme. However, inding the hopping distance of a labeling algorithm is not so diicult, and algebra veriies that ∑(i=1)2kdi =2k^2+2k-2, giving x(2k+1)=(k+1)2+(k-1)2. Even Length Paths We label the vertices of G as follows:
Table 5.2.1. Labelling Algorithm for P2k+1 where x1=0, and each xi is tight with x(i+1) for 1 ≤ i ≤ n-1. We proceed to show that this tight hopping is a distance labeling by checking it against the restrictions placed in Section 3.1. Note: his labeling only works for odd paths with n ≥ 7. In the case of n=5, x(2k+1) is too great with the above labeling. However, the case n=5 does follow the formula given above for rn(P5). Furthermore, all solutions generated with the above labeling have GT which are also path graphs. (See Section 7.2 for examples). Proof of Upper Bound Using our distance labeling veriication method for paths from Section 2.6.1 and Section 2.6.2, we need min(di, d(i+1)) ≤ n/2. Checking the labeling above, for every three consecutive vertices, at least one adjacent pair is k apart. As k < n/2, we see that the above labeling method is valid. Now, we need to show that the above method achieves the upper bound x(2k+1)=(k+1)2+(k-1)2. Since this is a tight hopping, there are no contributions from any vertices. herefore:
Table 5.2.2. Labeling Algorithm for P2k where x1=0, and each xi is tight with x(i+1) for 1 ≤ i ≤ n-1. We again check against the restrictions placed in Section 3.1 Proof of Upper Bound Once again, we need min(di, d(i+1)) ≤ n/2. Checking the labeling above, for every three consecutive vertices, at least one adjacent pair is k apart. As k < n/2, we see that the above labeling method is valid. Now, we need to show that the above method achieves the upper bound x2k=(k-1)2+k2. Since this is a tight hopping, there are no contributions from any vertices. herefore:
hen we get x2k=2k(2k-1)-∑(i=1)(2k-1)di. It is easy to verify that ∑(i=1)^(2k-1)di =2k2-1, giving x2k=(k-1)2+k2 22 | 2012-2013 | Volume 2
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As from Section 5.1 we got the lower bound of rn(Pn) for even and odd n, and have constructed cases achieving these lower bounds, thereby completing the proof to path graphs.
To maximize ∑(i=1)2kdi , we minimize the absolute values of the negative terms and maximize the values of the positive terms. here are two cases achieving this maximum summation:
Proof for Triangle Lollipops
Case 1: We have positive values of j belonging to {k+2, k+3,…, 2k-1, 2k, 2k}, each of which appears twice (note that this is 2k terms), negative values of j belonging to {1, 2,…, k-1}, each of which appears twice, and negative values for k and k+1 both appearing once.
heorem:
Lower Bound We continue to use the maximum hopping distance technique. Once again, there is a diferent maximum hopping distance for even and odd lollipops. Odd Length Lollipops Since:
if ∑(i=1)2kdi ≤ 2k2+2k-3, we have the desired lower bound. Claim: ∑(i=1)2kdi ≤ 2k2+2k-3 Proof of Claim: We use the property that d(xi, x(i+1))=|jj’| if xi=vj and x(i+1)=vj’ (and when xi=v(2k+1), j=±2k) for all distances except for d(v(2k+1), v2k) =1, as shown in Figure 6.1.1. However, we may show that∑(i=1)2kdi does not include d(v(2k+1), v2k).
Case 2: We have positive values of j belonging to {k+3, k+4,…, 2k-1, 2k, 2k}, each of which appears twice, positive values for k+1 and k+2, and negative values of j belonging to {1, 2,…, k}, each of which appears twice. In both cases, we get ∑(i=1)2kdi =2k^2+2k-3 as desired. Even Length Lollipops his case is more diicult than the odd length lollipop, an observation that is pretty intuitive that this case is more complicated after completing the proofs for the path graphs. Claim: ∑(i=1)2kdi ≤ 2k2-3 with 2 distinct values of i such that cb(xi)=1. Proof of Claim: Using the above logic, we know that we have 4k-2 terms of 1 ≤ j ≤ 2k, with k-1 positive terms appearing twice, k-1 negative terms appearing twice, and 1 positive and 1 negative term appearing once. he maximization of this sum occurs when we have positive values of j belonging to {k+1, k+2,…, 2k, 2k-1 , 2k-1}, each of which appears twice, a positive k appearing once, and negative values of j belonging to {1, 2,…, k-1}, each of which appears twice, and a negative k+1 appearing once. From this, we get:
Figure 6.1.1. Assigning Values of j on TL5 Suppose that ∑(i=1)2kdi does include d(v(2k+1), v2k). We notice that the remaining distances of the triangle lollipop are in fact equivalent to P2n. Since the maximum hopping distance of P2n was 2k2-1 we have ∑(i=1)2kdi=(2k21)+1=2k2. We proceed to construct a hopping with a greater distance. Since we have 2k+1 vertices, and 2 vertices are designated to be x1 and x(2k+1)}, we have (2k+1-2)∙2 +2 terms of j. hus, our sum has 4k terms, with 2k positive and 2k negative.
Now, we prove that the contribution to the maximum value is 2. Let the positive terms of j in our summation occur in region B, where j ≥ k+1, and the negative terms
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appear in region A, where j ≤ k.
Section 3.1
Figure 6.1.2. Calculating Contributions on TL2k
Note: his labeling only works for odd lollipops with n ≥ 7. In the case of n=5, the algorithm given above fails. However, the case n=5 does follow the formula given above. (run the code in Appendix B to Furthermore, all solutions generated by the algorithm have a consistent structure in GT.
Without loss of generality, let x1=vk. Now, if we consider xi=v1 then x(i-1) and x(i+1) both occur in region B. his gives min(di, d(i+1)) > (D+1)/2cb(x(i+1)) ≥ 1. In particular, cb(x(i+1))= min(di, d(i+1)) - (D+1 )/2 for min(di, d(i+1)) ≥ (D+1 )/2. hus, if min(di,d(i+1)) ≥ k+1, then, cb(x(i+1)) ≥ 2 and we are guaranteed the desired contribution of 2 to the maximum value. However, if min(di, d(i+1))=k→ x(i+1)=v(k+1), then cb(x(i+1))=1. In this case, we need to ind another pair of distances where both are at least k. Consider xj=v2. hen, to minimize cb(x(j+1)) we have xj=v(k+2). his again gives us cb(x(j+1))=1, so we have the two contributions to the maximum. Our current lower bound is rn(G) ≥ (2k-1)(2k-2+1)(2k2-3)+2= 2k2-4k+6.
Proof of Upper Bound Using our distance labeling veriication method for paths from Section 3.1, we need min(di, d(i+1)) ≤ (D+1)/2. Checking the labeling above, for every three consecutive vertices, at least one adjacent pair is k-1 apart. As k-1 < (D+1)/2, we see that the above labeling method is valid. Now, we need to show that the above method achieves the upper bound x(2k+1)=2k2-2k+3. Since this is a tight hopping, there are no contributions from any vertices. herefore:
hen we get x2k=2k(2k-1)-∑(i=1)(2k-1)di. It is easy to verify that ∑(i=1)(2k-1)di=2k2-1, giving x2k=(k-1)2+k2
Upper Bound Odd Length Lollipops We label the vertices of G as follows:
As from Section 5.1 we got the lower bound of rn(Pn) for even and odd n, and have constructed cases achieving these lower bounds, thereby completing the proof to path graphs. Now, we need to show that the above method achieves the upper bound x(2k+1)=2k2-2k+3. Since this is a tight hopping, there are no contributions from any vertices. herefore, x(2k+1)=∑(i=1)2k D+1-di. hen we get x(2k+1)=(2k+1-1)(2k-1+1)-∑(i=1)2kdi, and once again it comes down to ind the hopping distance of this speciic labeling scheme. However, inding the hopping distance of a labeling algorithm is not so diicult, and some algebra veriies that ∑(i=1)2kdi=2k2+2k-3, giving x(2k+1)=2k2-2k+3 Even Length Lollipops We label the vertices of G as follows:
Table 6.2.1. Labeling Algorithm for TL2k+1 where x1=0, and each xi is tight with x(i+1) for 1 ≤ i ≤ n-1. Similarly, we check against the restrictions placed in 24 | 2012-2013 | Volume 2
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REsEaRch Tightness Graphs
Introduction Tightness graphs, GT, are an interesting way to categorize solutions, as they reveal the underlying structure of a solution. One nice application of the tightness graph is its use in generating a labeling algorithm for new graph types. his could help generate an upper bound for the graph type in general. Using the Python code in Appendix B, we may view GT for several Pn. hen, we construct a labeling algorithm by comparing solutions with similar GT. he next subsection features several structures and examples of solutions and their corresponding GT. Structures and Examples Solutions of P2k+1 with graphs GT that are also paths:
Table 6.2.2. Labeling Algorithm for TL2k where x1=0, and each xi is tight with x(i+1) for 1 ≤ i ≤ n-1. Again, we check against the restrictions placed in Section 3.1 Note: his labeling algorithm only works for n ≥ 8. All cases TL2k with k < 4 were computed by the computer. Proof of Upper Bound We need min(di, d(i+1)) ≤ (D+1)/2. Checking the labeling above, for every three consecutive vertices, at least one adjacent pair is k-1 apart, except in those distance pairs where cb(x(i+1)) > 0. As indicated in the section about the lower bound, it is precisely vertices v1 and v2 for which cb(vi)=1. Now, we need to show that the above method achieves the upper bound x2k=2k2-4k+6. Since we have ∑(i=1) kdi=2k2-3, x(2k+1)=(2k-1)(2k-2+1)-(2k-3)+2. Some algebra reveals that ∑(i=1)2kdi=2k2-4k+6 as desired. So we have found rn(G) for the lollipop graph.
Figure 7.2.1. Graphs of solutions to P2k+1 and associated GT
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REsEaRch From this section, we conclude that inspecting the structures of GT is a quick way to identify and organize solutions on G.
Conclusion and Future Work
Figure 7.2.2. Solutions of P2k with graphs GT with a ladder structure
We have been able to establish a general method to inding the lower bound of any graph G using the idea of the maximum hopping distance. In Sections 5 and 6, we were able to ind these lower bounds for Pn and TLn and construct upper bounds that matched these values. he key contribution of this work is that it demonstrates a method by which rn(G) can be found and proven on a given class of graph. Additionally, we were able to use computer simulation to ind solutions to any new types of graph, and understand characteristics of these solutions. Further work is underway to ind rn(G) of the triangular lattice graph. hese graphs are of particular interest, because cellular systems generally have broadcasters located in the pattern of a triangular lattice. hus, determining the optimal frequency coordination of such a graph will be of greater application than for path graphs or lollipop graphs.
Acknowledgements I would like to thank Dr. Teague for his help and support of my work during this project. I was able to bounce a lot of ideas of of him, and he really helped me stay hopeful when it seemed as though my ideas had dried up.
References Figure 7.2.3. Graphs of solutions to P2k and associated graphs GT Notice that all of these solutions follow the labeling algorithm in Section 5.2.2. However, not all solutions to Pn have similar GT structures. Figure 7.2.3 shows two examples of alternative structures for even and odd length paths. Notice that they have diferent labeling algorithms from the examples in Figures 7.2.1 and 7.2.2. Instead, solutions with tightness graphs following structures from Figure 7.2.3 have separate labeling algorithms.
Figure 7.2.4. Graphs of solutions to P2k and associated graphs GT 26 | 2012-2013 | Volume 2
[1] D. Liu and X. Zhu, Multi-level distance labelings for paths and cycles, SIAM J. Disc. Math., in press, 2006. [2] J. A. Gallian, A dynamic survey of graph labeling, Electronic J. of Combinatorics, DS NO. 06, 16(2009). [3] J. R. Griggs, R.K. Yeh, Labeling graphs with a condition at distance two, SIAM J. Discrete Math. 5 (1992), 586â&#x20AC;&#x201C;595. [4] Molisch Andreas, Wireless Communications, WileyIEEE, New York, 2005.
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The Effect of Substrate Density on the Rate of Migration of NIH-3T3 Fibroblasts Elizabeth Tsui ABSTRACT Previous studies have suggested connections between the migration structures in normal cells known as podosomes and the migration machinery of cancer cells. Furthermore, recent studies contain evidence supporting a relationship between tissue density and metastatic cancer risk. Given that an increase in the risk for metastatic cancer is directly related to the rate of cell migration, this experiment explored the possible relationships between metastatic cancer risk (determined by cell migration rate) and collagen concentration (tissue density’s determining factor) through the use of NIH-3T3 ibroblasts. Fibroblast cells were seeded on top of hydrogels of collagen concentrations corresponding to the elastic moduli of normal and cancerous tissue (1.0mg/mL and 4.0mg/mL, respectively). hey were subsequently observed migrating into the hydrogels over a 5 to 6 hour time period, and average cell counts from the surface of each gel were noted at three time points separated by 1 or 2 hour incubation intervals. ANOVA revealed that: 1) collagen concentration does induce a signiicant diference on the number of surface cells present over time; but 2) the slopes of the linear its (i.e. rate of migration) were not shown to signiicantly difer between collagen concentrations. hese results suggest that the density of a substrate may have some efect on cell migration, without afecting migration rate.
Introduction Certain cells in the body have a natural ability to form specialized structures that enable cellular migration. For example, white blood cells (leukocytes) must degrade extracellular matrices (ECMs) and migrate through multiple tissue barriers in order to ight of infections and foreign pathogens. Another example is seen in a newly proposed model for cell invasion, C. elegans; in order for the organism to complete normal development, a cell must migrate through an extracellular matrix known as the basement membrane [1,2]. Originally named rosettes because of their appearances in interference reference microscopy, these migratory structures are now commonly known as podosomes [3]. Podosomes typically consist of an F-actin core surrounded by various adhesion proteins such as talin, vinculin, and paxilin, as well as integrins that allow the structures to bind to the underlying substrate [4,5]. As is commonly known, actin is a major class of microilaments, the components of the cytoskeleton responsible for cell motility. Podosomes are classiied according to their associated integrins, molecules that bind elements of the ECM and regulate ECM attachment [6]. hey allow adhesive structures to form a bridge between the ECM and the cell cytoskeleton [7]. β1 and β2 integrins are associated with podosomes and play critical roles in macrophage fusion [8]. However, when podosome-like protrusions were examined in a 3 dimensional environment, only β1 integrins were shown to associate with the protrusions [5]. Similar results noting diferences in podosome structure or morphology due to environmental factors have led to speculations suggesting that podosomes may play roles in helping cells sense their surrounding environments [9,10].
In order to migrate, cancer cells must detach from their original tumor sites and degrade the surrounding ECM. Interestingly, cancer cells form and extend similar F-actin rich protrusions known as invadopodia as the irst step in metastasis [11]. Invadopodia found in cancer cells are analogous to podosomes and are implicated in cancer’s deadly ability to metastasize. In contrast to the shallow extension of podosomes, however, invadopodia are usually found clustered together as large actin and cortactin dots burrowing deep into the ECM[12]. hey tend to be larger than podosomes, reaching measurements of 40µm2 (as compared to 0.4µm2) [7]. Invadopodia tend to penetrate their surrounding substrates very deeply, and thus are associated with a signiicantly more focused and higher rate of degradation than podosomes are. Like podosomes, they recruit metalloproteinases to degrade matrices; however, the invadopodia’s more aggressive migration tendencies have been attributed to its additional recruitment of serine proteinases [13]. Invadopodia have also been thought to have a role in helping the cell sense its environment. Studies have previously shown that tissue density may be related to the likelihood of developing cancer. For instance, a study in 2003 comparing a metastatic and nonmetastatic cancer found that an increase in collagen content was associated with tumor development [14]. Furthermore, research done by Provenzano et al. (2009) showed that an increase in collagen concentration caused an increase in matrix density, a condition which promoted a malignant phenotype. Changes in microenvironment corresponded with changes in density, i.e. an increase in density created a more ibrous microenvironment with fewer matrix pores, as well as an increase in matrix density and rigidity which, inally, proVolume 2 | 2012-2013 | 27
Street Broad Scientific moted an invasive phenotype [15,16]. Matrix rigidity is measured through the Young’s modulus, also known as the elastic modulus [16]. he Young’s modulus measures the stifness of an object by measuring a substance’s resistance to deformation when a force is applied, e.g. objects with high stifness such as glass and diamonds have high elastic moduli. A higher elastic modulus is also associated with an increased density due to the more ibrous microenvironment. Normal mammary tissue has an elastic modulus of 167 ± 31 Pa, while the tumor itself has a much higher elastic modulus of about 4049 ± 938 Pa [16]. However, the relationships of podosomes and invadopodia with their environments remain poorly characterized. As was shown by Van Goethem et al. (2011), the structure of podosomes varies widely with micro-environmental shifts. An example of this is the phenomenon of podosome group arrangement. In src-transformed cells, where the src tyrosine kinase is used to change the expression of a gene that codes for a component of podosomes, the podosomes form ring shaped structures known as rosettes. By comparison, in other cells, such as osteoplasts, podosomes tend to be arranged in clusters, showing the diversity of arrangement that podosomes possess in response to cell environment. Another study highlighting this phenomenon was done by Van Geoethem et al. (2011) using Matrigel, a gel that mimics a migratory cell’s typical environment. his study found that multiple podosomes are produced during migration, perhaps indicating that podosomes not only degrade matrices, but also seek out areas of lesser density in order to perform the most eicient matrix degradation. Expanding on this inding, Carman et al. (2007) found that during lateral migration of leukocytes, dozens of podosomes formed quickly along the endothelium to probe the surrounding environment. Over nuclei, the podosomes were quickly retracted without fully migrating into the substrate, leaving shallow “podoprints.” hus, a commonly purported hypothesis is that leukocytes use podosomes to locate areas of relatively low surface resistance in order to complete migration [9]. Regardless of recent progress, there are a number of remaining questions about podosomes that need to be addressed. For instance, what is the efect of substrate density on the migration behavior and structure of podosomes? Do cells tend to migrate faster or slower on substrates of difering densities? he answers to these questions could have important implications for our understanding of cancer metastasis, because if invadosomes do migrate preferentially because of density, variations in substrate density between tissues could be used to determine likely sites of metastasis [16]. To answer these questions, I observed NIH-3T3 Mouse Fibroblast cells seeded onto substrates of two collagen concentrations (4.0mg/mL and 1.0mg/mL) representing two substrate densities over a ive to six hour period to determine if substrate density has any efect on the rate of migration of the ibroblasts. Since previous papers 28 | 2012-2013 | Volume 2
REsEaRch have suggested a change in migratory response based on the cellular microenvironment, I expected that a diference in substrate density would: 1) create a diference in the mean number of cells present on the surface of the hydrogels over time; and 2) afect the rate of migration of cells from the surface into the hydrogels for both treatments, causing low density hydrogels to have rates of migration signiicantly diferent from high density hydrogels.
Materials and Methods My experiment consisted of two diferent treatments: the two collagen concentrations, 4.0mg/mL and 1.0mg/ mL, corresponding to high and low substrate densities, or cancerous and normal tissues, respectively. he concentrations were chosen based on previous literature from Paszek et al., 2005 and Provenzano et al., 2009. In order to ensure consistency with data collection, glass slides were uniform grids the size of 18mm x 18mm coverslips and labeled by hydrogel number and collagen concentration. To ensure uniformity in hydrogel size and shape, gel molds were made by wrapping glass coverslips with Carolina Observation Gel. Molds were then mounted onto the gridded glass slides and placed in Nunc cell culture dishes for gel formation (Figure 1).
Figure 1. Hydrogel molds in Nunc cell culture dishes mounted on top of gridded microscope slides. Gels for replicates 1 and 2 were made using Hystem cell culture scafold kits (Sigma-Aldrich) according to manufacturer’s instructions. Collagen concentrations for these replicates were prepared by adding 110µL of 4.0mg/mL or 1.0mg/mL collagen solution to 15mL centrifuge tubes. 250µL of each gel solution were then pipetted into the appropriate gel molds. For replicate three, Hystem-C cell scafolds obtained from Glycosan Biosystems were formed from a 7.5mL kit according to manufacturer’s instructions. To form the hydrogels with the low collagen concentration of 1.0mg/mL, 250µL of Gelin-S (reconstituted collagen concentration of 4mg/mL) were added to 15mL centrifuge tubes (Gelin-S is simply a powdered version of the collagen solution that was used in previous trials, suggested by the manufacturer as an alternative collagen source). 750µL of DG water was then added to the centrifuge tube
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REsEaRch to achieve a inal concentration of 1.0mg/mL; the tube was then mixed until a slightly viscuous, clear solution was obtained, and 1 mL of the solution was then added to a ready centrifuge tube. 500µL of the completed hydrogel was added to prepared gel molds. To form the hydrogels with the high collagen concentration (4.0mg/mL), 1 mL of Gelin-S (collagen concentration of 4.0mg/mL) was mixed with 1 mL of Hystem and 500µL of Extralink. 500µL of the complete hydrogel solution were pipetted into the appropriate gel molds and allowed to solidify. NIH-3T3 ibroblasts obtained from the Soderling Lab at Duke University were cultured in a 37°C CO2 incubator until the cells reached about 80% conluence. Cell density was then determined with a hemocytometer, while 500µL of cell slurry at a density of approximately 5000 cells/mL were added to the solidiied hydrogels; cells were allowed to attach for an hour (replicates 1 and 2) or two hours (replicate three) before taking the initial cell count. Starting cell counts for hydrogels at both concentrations were not statistically diferent from each other, as expected. Observation times were changed from 1 hour after initial incubation to 2 hours after initial incubation because cells from replicates one and two seemed to require more time to acclimate and attach to the hydrogels before the initial count, as is shown in Figure 2 below.
Figure 2. Cells on surface of hydrogels at 1 hour after initial incubation (left) and two hours after incubation (right).
licate (times are given in hours after initial incubation). Clumps of cells were counted as single cells to prevent bias towards higher cell counts. he mean cell counts of four randomly chosen boxes were used in calculating the means for each type of substrate, which was then plotted against time to determine rate of migration as represented by slope of linear it. he mean cell number and slopes of the linear its were then compared using JMP Student Edition 8 to determine statistical signiicance by ANOVA.
Results ANOVAs of mean surface cell counts from each hydrogel were performed to determine statistical signiicance. he mean number of surface cells in replicate one did differ signiicantly over time, indicating viability of ibroblast migration (i.e. the cells were able to form the structures necessary to initiate and continue migration into the hydrogels). his result was demonstrated in all replicates. Collagen concentration (representing substrate density) was shown in replicate one to have no signiicant inluence on mean cell counts over time (p= 0.367). his indicates that the mean number of surface cells is not afected by substrate density, which is inconsistent with the original hypothesis. However, the replicates two and three ofer opposing results. In replicate two, the ANOVA indicated that there was a signiicant diference in mean number of surface cells between high and low collagen concentrations (p = 0.0175), which supports my original hypothesis that manipulation of density would manifest a change between high and low collagen concentration substrates. Another interesting result lies in the shape of the graphed data. For both low and high collagen concentrations, the number of surface cells present over time seemed to decrease linearly, with RMSE values of 0.9958 (low) and 0.9941 (high). his suggests that while substrate density afects the overall rate of migration, it does not afect the linear trend in migration rate.
After one to two hours, the gels were observed and photographed at 100x with a Nikon inverted scope and attached Nikon D5100 DSLR. Surface cells were counted in four randomly chosen 20.25mm2 boxes over three two hour time intervals, given in the table below. Each hydrogel had four subsamples within the treatment, and all data analysis was performed with JMP Student Edition 8 (SAS). Time Point 1 2 3
Replicate 1 1 hour 3 hours 5 hours
Replicate 2 1 hour 3 hours 5 hours
Replicate 3 2 hours 4 hours 6 hours
Table 1. Description of observation time points by rep-
Figure 3. Time (hrs after initial incubation) was plotted against the average of surface cell counts obtained from each hydrogel by collagen concentration. A linear equation was plotted for hydrogels with a collagen concentration of 1.0mg/mL (low), yielding the model: y=6.6667x +44 (R2 = 0.9958). A linear it was also obtained Volume 2 | 2012-2013 | 29
Street Broad Scientific for hydrogels with a 4.0mg/mL collagen concentration (high). his analysis yielded the model: y = -8.4583x + 57.375 (R2 = 0.9941). Error bars represent Âą1 standard error. Similar relationships were seen in replicate three of the experiment. ANOVA showed signiicant diferences in surface cells present in regards to both time and collagen concentration with p<0.0001 and p<0.0081, respectively. his shows that not only did mean cell number decrease over time, the cell counts of the low hydrogel were signiicantly less than the number of surface cells present on the high collagen substrates, again supporting the original hypothesis that substrate density would have an efect on the mean number of cells present on the surface of the gels over time. Linear regression lines were again shown to it well with the decrease in mean surface cells over time, supporting the conclusion that the mean number of surface cells decreases linearly over time.
Figure 4. Time (hrs after initial incubation) was plotted against the average of surface cell counts obtained from each hydrogel by collagen concentration. A linear regresssion was plotted for hydrogels with a collagen concentration of 1.0mg/mL (low), yielding the model: y= -6.4125x + 45.067 (R2 = 0.9977) . A linear it was also obtained for hydrogels with a 4.0mg/mL collagen concentration (high). his analysis yielded the model: y = -7.65x + 54.75 (R2 = 0.9897). Error bars shown represent Âą1 standard error. he slopes of the linear regression lines for high and low collagen concentrations for replicates two and three were compared in JMP using a t-test. However, for both replicates two and three, the slopes were not shown to be signiicantly diferent between treatments. Previous experiments speculated that a change in substrate density would correspond to some change in migration; however, this report showed that while mean surface cell counts changed between collagen concentrations, the rate of decrease between treatments was not signiicantly diferent, thereby leading to the conclusion that mean surface cell count, but not rate of migration, was afected by substrate density. 30 | 2012-2013 | Volume 2
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Conclusion his experiment sought to address two questions: 1) what is the efect of substrate density on the rate of migration of NIH-3T3 ibroblasts; and 2) does manipulating the density of the cellâ&#x20AC;&#x2122;s environment (shown to have an efect on morphology) also afect how quickly cells move into a substrate? hree main conclusions can be drawn from this experiment. First, the mean surface cell number over time seemed to decrease linearly for both high and low collagen concentrations, meaning that substrate density had no obvious efect on the shape of the graph of the decrease in cell number. Second, analysis showed that substrate density afected the mean number of cells found on the surface of the hydrogel in two out of three replicates, largely supporting my original expectation. Interestingly, the data do not support the hypothesis that a change in substrate density would create a change in the rate of decrease of surface cells (i.e. the slope of the linear its compared between both concentrations), meaning that rate of decrease was not afected by substrate density. However, three possible circumstances may have led to error within this result. First, the low sample size within each replicate, decreased further within replicate two, may have increased variance overall. Moreover, while collagen is the largest component of the ECM, collagen is supported by a number of other substances, such as laminin, that may play a larger role in determining efect on migration. Furthermore, it is possible that the collagen concentration tested in this experiment did not present enough of a contrast to detectably alter the rates of cellular migration. As was seen in Provenzano et. al (2009), collagen concentration and elastic moduli of a substrate vary widely with an uncertainties ranging from 31Pa to 938Pa for normal and cancerous tissues, respectively. he collagen concentrations used in this experiment may have fallen within that error range, resulting in an inefective discrepancy in tissue densities and a concomitantly undetectable change in cellular migration rates. Future experimentation would include a larger sample size in order to decrease the amount of variance in data. Experimental systems for cell culture suspension could also be improved. For instance, as is shown in the left half of igure 2, there were certain areas in which cells may have been transferred in clumps which would lead to a decrease in cell count, since each clump was counted as one cell. Furthermore, since the boxes chosen for cell counts were random, there was no way of excluding those regions from data collection. To see if another substance plays a larger role in determining migration rate, a diferent ECM component could be tested in place of the collagen.. If collagen was used again, however, more collagen concentrations could be tested to see if there is a threshold collagen concentration that needs to be reached before achieving a change in migration behavior. Time interval observed could also be extended in order to see if the efects of sub-
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REsEaRch strate density require a longer time before they produce changes in migration behavior. Future experiments sandwiching the cells between instead of on top of substrates could assess the cell’s density preferences. By giving the cells a choice, regions more conducive to metastasis can be determined. If rate of migration is afected by a change in substrate density, then a literature search can be conducted to determine tissue densities throughout the body, and metastasis rates could subsequently be compared to see if the invadopodia of cancer cells are similarly afected. his could be the potential link tying podosomes, the migration machinery of normal cells, to invadopodia, the structures that equipcancer with the ability to metastasize. While this explanation may not encompass the whole picture, identiication of factors determining the direction of cell migration would represent a key advancement in the ight against cancer. Ideally, these podosomes could be the key to detecting potential sites of metastasis. However, applications of this knowledge can only be made possible with further research. As of now, the steps leading to fully developed invadopodia are unclear. For instance, does the formation of an F-actin core spark the development of the invasive protrusion by gathering surrounding metalloproteinases, or does the gathering of the proteins lead to the development of an F-actin core [8]? Other questions could address the implications of diferences between podosomes in diferent cell types, i.e. are the same properties universally affected in all cell types? Also, it is still unknown whether invadopodia are truly related to podosomes. As a concrete deinition of both is lacking, it is diicult to say whether the relationship between these two cellular components is truly homologous, or only supericial. Even with all of the remaining questions, possible experimental models for learning more about podosomes, invadopodia, and their roles in tissue invasion have already been proposed. he Invadosome Consortium, a group of scientists committed to learning more about these structures, is making great strides in the elucidation of these organelles. Together, these and other initiatives are slowly but surely increasing what is known about the structures that have the potential to be incredible targets in the ight against cancer.
Acknowledgements I would like to thank Soderling and Blobe Labs at Duke University for providing NIH-3T3 Fibroblasts. Additionally, Dr. Amy Sheck and Korah Wiley, North Carolina School of Science and Mathematics, for invaluable assistance and advice. Research in Biology Peers: Ian Maynor, William Ge, Jordan Harrison, Chelsey Lin, Ashwin Monian, Jackson Mower, Aakash Gandhi, Hun Wong, Mark Kirollos, and Natalia Von Windheim for their advice and suggestions. I would also like to thank Nathaniel Doty, Glycosan Biosystems, for assistance with hydrogels. Fi-
nally, I give my thanks to the Glaxo Endowment to NCSSM for research funding.
References [1] Carman, C.V. 2009. Mechanism for transcellular diapedesis:probing and pathindng by ‘invadosome-like protrusions’. Journal of Cell Science 122: 3025-3035. [2] Hagedorn, E. J. and D. R. Sherwood. 2011. Cell invasion through basement membrane: the anchor cell breaches the barrier. Current Opinion in Cell Biology 23:1-8. [3] Pfaf, M. and P. Jurdic. 2001. Podosomes in osteoclast-like cells:structural analyis and cooperative roles of paxillin,proline-rich tyrosine kinase 2 (Pyk2) and integrin αVβ3. Journal of Cell Science 114: 2775-2786. [4] Gavazzi, I., M. V. Nermut, and P. C. Marchisio. 1989. Ultrastructure and gold-immunolabeling of cell-substratum adhesions (podosomes) in RSV-transformed BHK cells. Journal of Cell Science 94: 85-99. [5] Van Goethem, E., R. Guiet, S. Balor, G. M. Charriere, R. Poincloux, A. Labrousse, I. Maridonneau-Parini, and V. Le Cabec. 2011. Macrophage podosomes go 3D. European Journal of Cell Biology 90:224-236. [6] Hynes, R. 2002. Integrins: bidirectional, allosteric signaling machines. Cell 110: 673-687. [7] Linder, S. 2009. Invadosomes at a glance. Journal of Cell Science 122: 3009-3013. [8] McNally, A. K. and J. M. Anderson. 2002. β1 and β2 integrins mediate adhesion during macrophage fusion and multinucleated foreign body giant cell formation. American Journal of Pathology 160: 621-630. [9] Carman, C.V, P. T. Sage, T. E. Sciuto, M. A. de la Fuente, R. S. Geha, H. D. Ochs, H. F. Dvorak, A. M. Dvorak, and T. A. Springer. 2007. Transcellular diapedesis is initiated by invasive podosomes. Immunity 26: 784-797. [10] Carman, C.V. and T. A. Springer. 2008. Trans-cellular migration: cell-cell contacts get intimate. Current Opinion in Cell Biology 20: 533-540. [11] Condeelis, J., and J. E. Segall. 2003. Intravital imaging of cell movement in tumours. Nature Reviews Cancer 3: 921-930. [12] Linder, S. 2007. he matrix corroded: podosomes and invadopedia in extracellular matrix degradation. TRENDS in Cell Biology 17: 107-117. [13] Artym, V.V, Y. Zhang, F. Seillier-Moiseiwitsch, K. M. Yamada, and S. C. Mueller. 2006. Dynamic interactions of cortactin and membrane type 1 matrix metalloproteinase at invadopodia: deining the stages of invadopodia formation and function. Cancer Research 66: 3034-3043. [14] Akiri,G., E. Sabo, H. Dafni, Z. Vadasz, Y. Kartvelishvily, N. Gan, O. Kessler, T. Cohen, M. Resnick, M. Neeman, and G. Neufeld. 2003. Lysyl oxidase-related protein-1 promotes tumor ibrosis and tumor progression in vivo. Cancer Research 63:1657-1666. Volume 2 | 2012-2013 | 31
Street Broad Scientific [15] Paszek, M. J., N. Zahir, K. R. Johnson, J. N. Lakins, G. I. Rozenberg, A. Gefen, C. A. Reinhart-King, S. S. Margulies, M. Dembo, D. Boettiger, D. A. Hammer, and V. M. Weaver. 2005. Tensional homeostasis and the malignant phenotype. Cancer Cell 8: 241-253. [16] Gillette, B.M., N. S. Rossen, N. Das, D. Leong, M. Wang, A. Dugar, S. K. Sia. 2011. Engineering extracellular matrix structures in 3D multiphase tissues. Biomaterials: doi:10.1016/j.biomaterials.2011.05.043. Accessed: April 19, 2012.
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Chitosan-modiied Cellulose as Adsorbent to Collect and Reuse Nitrate from Groundwater Christie Jiang ABSTRACT Nitrate pollution of water systems in the United States continues to increase, presenting hazards to humans and the environment. To remove this extremely soluble ion contributed largely by synthetic agricultural fertilizers, a cost-eicient and resource-eicient method, adsorption, has great potential compared to other options. In this study, chitosan, which becomes protonated in acidic solution, was combined with cellulose derived from cardboard. his combination of polymers yields a positively charged surface to attract nitrate. Batch studies revealed chitosanmodiied cellulose to improve adsorption capacity from 0.3356 to an average 3.124 milligrams of NO3- per adsorbent mass. Using the Langmuir isotherm, linear regression was performed to describe the adsorption characteristic. Based on the it, efective adsorption increases as more adsorbent is present, producing a relationship between adsorption site availability and resulting concentration due to increased aggregate charge and attraction to nitrate ions. Desorption was evaluated, with chitosan-modiied cellulose releasing .294 â&#x20AC;&#x201C; 4.7% of adsorbed amounts, indicating possibility of slow-release fertilizer use as the organic polymers decompose in soil. Compared to related materials, the investigated adsorbent had more environmentally friendly and adsorptive properties, as well as simpler production. Larger scale studies and optimization of the cellulose-chitosan ratio will improve further upon this research.
Introduction From 1988 to 2004, the proportion of wells in the United States exceeding the national limit of nitrate concentration increased from 16 to 21 percent. Wells account for about 15 percent of the public water supply; this translates to over 3 percent of the population drinking water containing excess nitrate [1]. Nitrates can cause medical complications if consumed as well as undesirable phenomena in the environment such as eutrophication [2]. he increasing presence of nitrates in fresh water wells is a growing concern; managing the nitrogen cycle has been named one of fourteen â&#x20AC;&#x153;Grand Challengesâ&#x20AC;? by the National Academy of Engineering [3]. Recent comprehensive assessments on nitrate pollution have also sparked media attention related to nitrate pollution of groundwater, shown to afect the quality of life of whole communities at a time [4]. he United States Environmental Protection Agency (USEPA) has set the Maximum Contaminant Level, or MCL, to 10 mg/L nitrate as nitrogen [5].Recent trends indicate increasing levels of nitrates. As part of the nitrogen cycle, nitrate emerges from both direct input to the soil and conversion from other nitrogen-based compounds, such as ammonia. Nitrate becomes toxic when converted to nitrite in the human body, leading to medical problems such as methemoglobinemia, or blue baby syndrome, as well as higher risk for thyroid cancer. One study of a primarily farming-based community has reported health complications that may have stemmed from unusually high nitrate concentrations in the local water. he same report concluded that 96 percent of nitrate pollution in the area had come from agricultural sources [4]. Concentrations in the rest of the nation show no signs of stabilizing as agricultural demands along with fertilizer input continue to rise (Figure 1).
Figure 1. Jagged line shows historical and sampled data concerning input from fertilizer. Plotted points indicate the increase of number of wells over the MCL, a trend set to increase along with nitrogen input [1]. Nitrate Removal Techniques he complexity of soil and water processes and the variety of substances involved make it diicult to pinpoint methods to begin uncovering the most efective means of water remediation. Current large-scale methods of decontamination are typically incomplete in removal, unreliable, or dependent on high amounts of resources or power, and are therefore costly [2]. Nitrate removal presents an even more challengingproblem; because it is extremely soluble, basic precipitation and iltration techniques are inefective. Current technologies for treating nitrate-contaminated water include ion exchange, reverse osmosis, and electrodialysis [6]. hese and other techniques are generally expensive and Volume 2 | 2012-2013 | 33
Street Broad Scientific not suiciently efective, often complicating processes and factors involved. Of those listed, ion exchange presents the least cost and technology-intensive option, but it involves releasing some other like-charged substance as the pollutant is taken in. Consideration of Nitrate Absorption Recent studies suggest that an even simpler and more efective technique, adsorption, or attraction to the surface of a material, ofers great potential [6]. Because nitrates readily leach from soil and dissolve completely in water, extensive research has and continues to be done to ind adsorbents that ofer an environmentally friendly, reusable, and cost-eicient solution to lessening the problem of nitrate pollution. his makes it much more desirable, but still no feasible nitrate adsorbent has been found. Due to the promising premise of nitrate adsorption as the future primary method of decontamination, a variety of materials have been investigated. hough there is no doubt that engineered substances, such as activated materials and altered clays, could be very efective nitrate adsorbents as well, this would be counterproductive as adding new materials would potentially cause even more problems to be addressed. In a review detailing advances made in phosphorus removal, it is suggested that, ideally, the pollutants removed would be able to be used as raw materials for fertilizer [7]. In the case of nitrates, if this were accomplished, a sustainable cycle would be achieved as it would not be necessary to exploit new resources. Among natural adsorbent possibilities, categories include carbon-based sorbents, natural sorbents, biosorbents, waste materials, and miscellaneous [6]. he wide range indicates widespread uncertainty on the topic as to what an ideal adsorbent would entail. But one very common waste product that was not listed as having been researched is paper, which is present in signiicant volumes and accessible for studies. Waste paper comes in many forms and in large volumes, making it an attractive possibility. After saturation, it could be repurposed for agricultural use. In addition, it ofers potential for both physical and chemical modiication. A variety of other adsorbents have been investigated, and paper could be a viable option, having comparable surface area, texture, and chemically unreactive composition. Paper is relevant to previously mentioned materials not only in terms of being environmentally friendly but also in terms of composition and properties. he characteristics of a desirable adsorbent are signiicant surface area and volume on and in which the target substance can collect. Cellulose, the main component of paper, has been experimentally determined as one with high values for desirable adsorbent characteristics as compared to other similar ibers. Using Brunauer, Emmett, and Teller (BET) theory which determines physical adsorption on a surface, and related isotherms, surface area of 0.45 square meters per gram and total micropore volume of 0.50 cubic millimeters per gram of cellulose were determined [8]. Although 34 | 2012-2013 | Volume 2
REsEaRch these values are not comparable to those of, for example, activated carbon or organoclays, which have values greater by several orders of magnitude, they are suicient to indicate potential for improvement and use as adsorbents. Cellulose has a slightly negative surface charge due to outer hydroxyl groups (Figure 2.a) that would repel likecharged negative nitrates, and as a polymer it is not strong enough to induce ion exchange. herefore modiication of surface charge is necessary. Chitosan, a natural polymer of dried crab and shrimp shell matter, can become positively charged when its outer amino groups (Figure 2.b) are protonated, which suggests potential for nitrate removal as it has been reviewed for efective removal of the negative chromate ion [9]. (a)
(b)
Figure 2. (a) Structure of cellulose unit with hydroxyl groups, (b) structure of chitosan unit with hydroxyl groups and amino groups [10]. To this end, a need for efective and green nitrate removal technology is evident, and the proposed nitrate adsorption method may hold great potential. his study aimed to develop an efective adsorbent of nitrate based on the principles of simplicity and sustainability from cellulose and chitosan and evaluate the material for its properties and possibility of repurposing.
Materials and Methods Preparation of Cellulose Adsorbent A plain used corrugated cardboard box was chosen to be the source of the cellulose base for the adsorbent. he wide availability of cardboard and often minimal ink coverage made it an ideal candidate for the study. he cardboard was cut to approximately centimeter square pieces and broken down to a pulp by soaking in water. Prior to proceeding, in order to evaluate any possible chemicals already involved in the cardboard, precipitation tests were done. Small drops of AgNO3; HNO3 and (NH4)2MoO4; BaCl2; and NaOH were added to test for chlorides, phosphates, sulfates, and ammonium, respectively. Precipitation was minimal for all tests, indicating low concern for interfering and polluting ions. After soaking, the cellulose samples were then processed in a blender into pulp. he pulp was dried on a iberglass screen, and inal particle size after drying was
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REsEaRch reduced to around 40 mm3 volume and 68 mm2 surface area. Practical grade chitosan from Sigma-Aldrich was used to make a 1% chitosan by mass and 1% acetic acid by volume solution [11]. he protonated chitosan amino groups from -NH2 to -NH3+ in the presence of acid made it soluble as well as positively charged. he mixture was stirred at 80째C until the chitosan was dissolved completely, forming a viscous solution. After it was cooled, the same soaking and pulping procedure for cardboard was used as had been for the water pulped cellulose. Dried chitosan-modiied cellulose formed a thinner and more brittle sheet, so inal particle size was reduced to the same amount of approximately 68 mm2 surface area but only 24 mm3 volume. Preparation and Measurement of Nitrate Adsorbent Because potassium is another common chemical used in conjunction with nitrates in fertilizer, solid crystal KNO3 was used throughout the experiment to make the standard nitrate solutions to be relatively realistic to actual environmental situations. Units of nitrate as nitrogen were used for concentration. he concentration of 50 mg/L NO3 as N was made and used for batch studies. A Vernier Nitrate Ion-Selective Electrode (ISE) probe was used to measure nitrate as nitrogen concentration and calibrated by voltages from standards of 100 and 1 mg/L NO3 as N. In all concentration and mass values of nitrate, the units used were in NO3 as N. LoggerPro software was used to collect data from the probe. Batch Studies Adsorption experiments were primarily carried out using batch studies. To generalize the characteristic of the plain pulped cellulose, approximately 0.2 grams of adsorbent were added per 50 milliliters of equal concentration nitrate solution in a beaker for each sample. An individual magnetic stirrer for each beaker was set to 600 rpm, and samples were stirred for one hour. Samples from each beaker were then centrifuged and left for more contact time for at least 48 hours. hey were then centrifuged once again. Before data collection, ammonium sulfate ionic strength adjuster (ISA), 2M (NH4)2SO4, was added in the ratio 2:100 to the sample volume to reduce possible measurement interference from other content in the sample. During collection, the ISE was held in place in each centrifuge tube for at least one minute for values to equilibrate. Samples from batch studies needed to be centrifuged before measurement so that the ISE would only come in contact with solution and not pieces of adsorbent. Overall, the contact time for samples was maintained to be approximately 48 hours as described previously, but variations of centrifuge procedures were tested for efectiveness. hree methods, denoted as CB1, CB2, and CB3 (where CB stands for cardboard) to indicate use of plain cardboard, were used. Samples using CB1 method were centrifuged
immediately after mixing but left alone for the remainder of the time. Samples using CB2 were not centrifuged until after the 48 hours, and those using CB3 were centrifuged both immediately and after sitting, as done originally. To compare unmodiied and modiied cellulose, batch studies were run with all nitrate samples originating from one 500 milliliter lask to ensure standardization. 0.2 grams of both kinds of adsorbent were used per 50 milliliters of solution. As before, all samples were stirred, centrifuged, left, and centrifuged again. ISA was added before measurement. Adsorption Isotherms To characterize the adsorptive behavior of cardboard cellulose with chitosan, the ratio of adsorbent-to-adsorbate was varied by running batch studies once again and changing adsorbent dosage. Adsorbent masses of 0.1002, 0.1992, 0.3007, 0.4992, and 0.6994 g were used. he relationship of dosage and adsorptive capacity to inal concentration was analyzed by applying two major adsorption models, as done in published adsorption analyses [12]. he Freundlich isotherm is the most basic adsorption isotherm and describes the amount of adsorbate per adsorbent as a function of the resulting solution concentration. he isotherm is deined as
and the linear form as
where x is mass of adsorbate adsorbed per mass of adsorbent, C is solution concentration after adsorption, and K and 1/n are constants. he Langmuir isotherm takes some more speciic assumptions into consideration. In particular, the Langmuir isotherm hypothesizes uniform monolayer capacity for the adsorbent, or equal capability of all sites to adsorb. Also included is the assumption that adsorbed molecules do not interact or deposit on each other. he isotherm is deined as
and the linear form as
where x and C are the same as in the Freundlich isotherm, and xm and K are constants. In particular, xm denotes the maximum x in a monolayer of adsorbate on adsorbent. he R2 value for the linear it of each model was considered, and the better it used to evaluate the adsorptive behavior of the cellulose with chitosan.
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Street Broad Scientific Desorption Experiment for Reuse Adsorbent pieces contained in samples from the comparison of unmodiied and modiied cellulose were dried on iberglass screen. If the pieces were to be added back into the ground as fertilizer, the characteristic of nitrate leaching out would be important. In order to examine possible signiicance in reusability, the adsorbents were placed in separate beakers of 6 mL of water. After three days of soaking, the nitrate as nitrogen concentration of the water was measured as a representation of desorption, to be compared to the original amounts adsorbed by the particles and considered for efectiveness of nitrate reuse.
REsEaRch adsorbed per gram adsorbent.
Results and Discussion Efect of Centrifuge Methods he data collected directly from the nitrate probe produce plots of concentration versus time (Fig. 3), which cannot be easily directly used. For the study of the efect of centrifuge methods, as in subsequent studies, several steps were taken to obtain a more useful form. he mean concentration was calculated for each sample, but only considering the latter 75% of the data for each sample. hat is, the values measured from the irst 25% of total time for each sample were disregarded to allow the nitrate ISE to reach stability. In particular, measurements related to the previously mentioned CB1, CB2, and CB3 methods were analyzed. Once singular data points associated with each sample contained within each set of data were determined, they were then used in their respective analyses.
Figure 4. Average concentrations of solutions using different centrifuge methods.
Table 1. Comparison data for centrifuge methods. he data indicates CB3 as the most efective procedure because centrifuging samples multiple times may improve adsorption, as the adsorbent and the nitrate that has already been collected on it are physically separated from the solution to decrease homogeneous desorption while still allowing for extended contact time. CB3 was used for all subsequent batch studies. hough of these methods CB3 produced the greatest adsorptive result, a ratio of approximately 1 milligram adsorbed per gram of adsorbent leaves much room for improvement.
Figure 3. Format of raw data when measuring concentrations (mg/L NO3) for centrifuge methods CB1, CB2, and CB3 using ISE. Comparison he average concentrations for samples run with no CB, CB1, CB2, and CB3 are shown in Fig. 4. To appropriately compare the methods, the concentrations were converted to mass of nitrate as nitrogen to then evaluate milligrams 36 | 2012-2013 | Volume 2
Figure 5. Average concentrations of solutions with unmodiied and modiied CB. Error bar indicates standard deviation of concentrations with chitosanmodiied CB.
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Table 2. Speciic data for unmodiied and modiied cellulose comparison.
Table 3. Data for isotherm analysis, using various CB dosages.
Table 4. Modiied values for linear it. * Outlier not used in isotherm analysis.
Table 5. Adsorption isotherm values.
Figure 6. (a) Linear Freundlich it, with Log(C) along x-axis, Log(x) along y-axis, and R-squared = .7917; (b) linear Langmuir it, with 1/C along x-axis, 1/x along y-axis, and R-squared = .9596, indicating a better it statistically. Volume 2 | 2012-2013 | 37
Street Broad Scientific Efect of Chitosan Modiication Multiple samples of both unmodiied and modiied cellulose were run using initial concentration taken from one lask of 50 mg/L NO3 solution. he results (Figure 5) indicate the addition of chitosan to greatly improve nitrate uptake. A typical, standard sample of unmodiied cardboard is compared to samples with chitosan content, showing that an over 10% reduction in concentration is possible. he relative strength of cellulose adsorbent with chitosan is greater than that of just cellulose. he attractive nature of a positive chitosan surface charge as compared to the negative nitrate ions is likely the explanation for the improvement. he standard deviation of 3.48 mg/L NO3 in post adsorption concentrations is due to the non-uniformity of samples. he inal sample of cardboard with chitosan produced a signiicantly higher concentration reading though it was taken from the same batch in the same beaker as other samples. As samples were transferred from beakers to centrifuge tubes, the distribution of cardboard pieces throughout solution was not homogeneous, causing the adsorbent-to-adsorbate ratio to be varied during the extended contact time. But even with such variations in amount of adsorbent, a signiicantly higher proportion of nitrate was adsorbed with the presence of chitosan, ranging from 13.495 to 32.087% as compared to plain cardboard which in this case adsorbed 2.713% of total nitrate. Adsorption Isotherms Tables 3 and 4 provide data produced given ive different amounts of cellulose and one standard sample. he dosage of 0.1002 g produced values that were ultimately not used in the isotherms due to the uncertainty of transferring batch solutions to centrifuge tubes, where the low mass of adsorbent would lend to less certain homogeneity in mixture. Data indicated this uncertainty, as the value contributed an extreme outlier. Linear its were performed for Freundlich (Equation 1.2) and Langmuir (Equation 2.2) isotherms, depicted in Figures 6.a and 6.b, respectively. he linear regression values are given in Table 5, corresponding to the respective notation. he Langmuir it was better suited to the data as given by its R-squared value of .9596 as opposed to that of the Freundlich it, .7917. he Freundlich isotherm is empirical, involving only the concentration and two constants, and therefore too simple to appropriately model the data. Interrelated constants and more parameters likely made the Langmuir isotherm more mathematically appropriate for the data. Because the Langmuir regression exhibited a better linear it, the values were taken and placed into the original equation (2.1), giving
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which has a modiied form and is shown graphically in Figure 7. However, the maximum adsorbate per site, xm, value is lower than the plotted x values, so it appears that, in this case, the data does not follow Langmuir assumptions. From the raw data, it was clear that concentration generally decreased as more adsorbent was added. However, the isotherms compare sites illed per adsorbent and resulting concentration, not just amount of adsorbent and concentration. In an ideal Langmuir it, increasing concentrations would indicate increased proportion of adsorbate per adsorbent as more sites on the adsorbent would be available to be used. But the data presented suggests an inverse proportionate relationship instead. For the chitosan-modiied cellulose, surface area and volume did not necessarily increase proportionately with mass as they were lat pieces as opposed to rough particles. herefore it would be inaccurate to suppose that the amount of nitrate adsorbed would decrease accordingly with increasing adsorbent mass due to more open adsorption sites. Instead, the it seems to suggest the cellulose with chitosan become more and more apt to adsorb as more adsorbent is available and attractive (with greater aggregate charge) to the nitrate, rather than having a set number of sites that compete with each other.
Figure 7. Inverse proportionate relationship between C, resulting concentration after adsorption, on the x-axis and x, amount adsorbed per mass adsorbent, on y-axis given by Langmuir isotherm. Desorption for Reuse To evaluate the possibility of re-releasing the adsorbed nitrate into the ground by reusing adsorbent as fertilizer material, desorption was measured for used samples of plain and chitosan-modiied cardboard cellulose. he plain adsorbent exhibited much higher desorption proportions than the modiied adsorbent, in part due to the much less ibrous and more rigid structure of the latter. Total adsorbed mass and adsorbent mass were used from Table 3 to calculate desorption values. A standard concentration was measured from the water used to soak the samples to calculate amount desorbed.
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Table 6. Desorption of used samples. he extreme polarity in desorption rates indicates a signiicant diference in the properties of the cellulose after modiication. hough this experiment were originally run to identify promising desorption rates for nitrate re-release in soil, the low desorption rates in fact ensure another desirable quality. he behavior of plain cardboard would suggest only very temporary adsorption because if left in solution for extended periods of time, the nitrate would leach back out. Cardboard with chitosan, on the other hand, would attract and keep adsorbed nitrate. Once added to soil, decomposition of the adsorbent as a whole would slowly release nitrate, due to the organic nature of both cellulose and chitosan, allowing for various practical applications. Comparison to Other Absorbents he maximum mass of nitrate adsorbed per mass adsorbent (“x”) compared to existing data for other adsorbents indicates the signiicance of this study in the context of all nitrate adsorption experiments. he maximum “x” was not found through Freundlich or Langmuir because the characteristic of the chitosan-modiied cellulose adsorbent did not follow the basic assumptions of the isotherms. hus considering the highest “x” values in multiple batch studies, including the adsorption study, it appears that 0.200 g adsorbent per 50 mg/L NO3 as N solution caused the most eicient adsorption and gives the closest estimate to xm. Published papers cover a multitude of techniques and materials tested for nitrate adsorption. But many of these involve synthesis (chitosan beads, carbon cloth, organoclays) or addition of chemicals (HCl activation, dimethylamine), some of which are reactive (epichlorohydrin, which forms a carcinogen in water). Such adsorbents would not be fairly compared to the adsorbents in this research, which use existing waste and only adds a natural polymer. herefore only xm values for adsorbents comparable in either principle or materials were considered for comparison.
Untreated natural waste products exhibit lower adsorption potential, while synthesized chitosan beads show immense adsorptive strength. Combining these two situations gives the slightly improved capacity of cellulose with chitosan. Desorption data is unknown for these materials, but it is unlikely that chitosan hydrobeads, which are less cost-eicient, more complex to form, and involve no recycled materials, would be reused in a fertilizer. herefore, compared with the possibly reusable adsorbents mentioned in published work, the adsorbent produced in this research has considerable adsorptive strength, as well as further implications for environmental sustainability.
Conclusion Cellulose waste in the form of cardboard was successfully characterized as an adsorbent for aqueous nitrate. Modiication with chitosan improved cellulose adsorption in a standardized experiment from adsorbing 2.713% to an average of 24.97% of nitrate in solution. An adsorption isotherm study was then performed, but ofered inconclusive results for describing the mechanism of the adsorbent, though a trend was identiied. Low rates of desorption were determined for the cellulose with chitosan, which suggests the possibility of slow-release fertilizer use in application. Minimal desorption in solution is also promising in that adsorbed material will remain on the adsorbent even if exposed to water for extended amounts of time. he maximum determinable adsorption capacity from the study was compared to values in published work. Only research involving natural materials, such as bamboo and straw, was considered, as those with added chemicals would increase cost environmental complications. he cardboard and chitosan adsorbent made in this study exhibited more eicient adsorptive behavior than such published work. High adsorptive capacity of synthesized chitosan beads was also considered, as it suggests improved chitosan and cellulose integration could improve adsorption as well. Positive implications for the use of chitosan-modiied cellulose include decreasing paper waste, minimizing addition of environmental hazards, reuse as fertilizer, and appreciable adsorptive ability. Column studies have been planned to be carried out to scale up the research. he results of such research would then indicate possibility of using the cellulose pieces in water iltration systems, groundwater and well treatment, or integration into other processes. A wide range of applications exist, especially because the chitosan-modiied cellulose shows considerable aptitude to adsorbing nitrate.
Table 7. Adsorptive capacity xm of published adsorbents versus that of this study. *Average of all x values given batch of 0.200 g CB per standard solution. Volume 2 | 2012-2013 | 39
Street Broad Scientific Acknowledgements I would irst like to thank Dr. Myra Halpin for guidance and inspiration through the Research in Chemistry program at the North Carolina School of Science andMathematics (NCSSM) which provided me with lab space. Dr. Halpin sparked my interest in environmental science, speciically nitrate pollution, and helped me develop the project. I would also like to thank Dr. Monique Williams from NCSSM for supervising the project for several weeks. Next, I would like to thank Dr. Martin Hubbe and Dr. David Genereux from the North Carolina State University for answering questions I had for them on adsorption studies, materials, and groundwater treatment. I also have many thanks to my peers in the Research in Chemistry program as they also provided invaluable encouragement and input throughout the project. Last but not least, I would like to thank my family for support throughout this venture as I approached the project largely independently.
References [1] Dubrovsky, N.M., and P.A. Hamilton (2010). Nutrients in the Nation’s Streams and Groundwater: National Findings and Implications. U.S. Geological Survey Fact Sheet 2010-3078, 6. [2] Sparks, D. L. (1995). Environmental Soil Chemistry. San Diego, CA: Academic Press. National Academy of Engineering (2008). Manage the nitrogen cycle. NAE Grand Challenges for Engineering. Retrieved September 25, 2012 from http://www.engineeringchallenges.org/cms/8996/9132.aspx. [3] Holbrook, S. (2012). Farming Communities Facing Crisis Over Nitrate Pollution, Study Says. Food & Environment Reporting Network. Retrieved September 25, 2012 from http://thefern.org/2012/03/farming-communities-facing-crisis-over-nitrate-pollution-study-says/. [4] United States Environmental Protection Agency (2012). Drinking Water Contaminants. Retrieved September 25, 2012 from http://water.epa.gov/drink/contaminants/index.cfm. Bhatnagar, A., & Sillanpää, M. (2011). A review of emerging adsorbents for nitrate removal from water. Chemical Engineering Journal, 168, 2, 493-504. [5] de-Bashan, L.E., & Bashan, Y. (2004). Recent advances in removing phosphorus from wastewater and its future use as fertilizer (1997–2003). Water Research, 38, 19, 4222-4246. [6] Bismarck, A., Aranberri-Askargorta, I., Springer, J., Lampke, T., Wielage, B., Stamboulis, A., Shenderovich, I. & Limbach, H. (2002). Surface Characterization of Flax, Hemp and Cellulose Fibers; Surface Properties and the Water Uptake Behavior. Polymer Composites, 23, 5. 40 | 2012-2013 | Volume 2
REsEaRch [7] Hubbe, M.A., Hasan, S. H., & Ducoste, J. J. (2011). Cellulosic Substrates for Removal of Pollutants from Aqueous Systems: A Review. 1. Metals. BioResources 6, 2161-2287. [8] Royal Society of Chemistry. Cellulose and Chitosan chemical structures. ChemSpider. Retrieved on September 25, 2012 from http://www.chemspider.com/ChemicalStructure.26943876.html and http://www.chemspider. com/Chemical-Structure.2342878.html?rid=b656acee9c8e-4951-9c69-480347c7db87 [9] Urreaga, J.M., & de la Orden, M.U. (2006). Chemical interactions and yellowing in chitosan-treated cellulose. European Polymer Journal, 42, 10, 2606-2616. [10] Okeola, O. F. & Odebunmi, E. O. (2010). Comparison of Freundlich and Langmuir Isotherms for Adsorption of Methylene Blue by Agrowaste Derived Activated Carbon. Advances in Environmental Biology, 4, 329-335. [11] Mizuta, K., Matsumoto, T., Hatate, Y., Nishihara, K., & Nakanishi, T. (2004). Removal of nitrate-nitrogen from drinking water using bamboo powder charcoal. Bioresource Technology, 95, 3, 255-257.
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Generation of Electricity from the Wind Draft of Cars Harish Pudukodu ABSTRACT We developed a theoretical turbine power output model dependent on automobile speed and turbine distance from cars. Analysis of the data from experimental ield tests with rush hour traic, controlled single-car testing, and CFD modeling showed that our turbines generated electricity, but did not support our theoretical model, which assumed laminar low and spherical cars. Our study represents a creative implementation of wind power that may have signiicant economic/environmental implications for the future of renewable energy.
Introduction
method a few assumptions must be made. he assumptions are that the low is irrotational, the low is axisymmetric, the low is laminar, and the object that is causing the low is a sphere moving in the luid ield. According to these assumptions, the formula for velocity potential (as expressed through polar coordinates) is the following:
Motivation In the current era, energy is primarily obtained from coal and oil [1]. Yet, as these sources run thin, the world must look toward more sustainable sources of energy, such as renewable energy. One major form of renewable energy, and the form that is the topic of this research project, is wind energy. Currently, wind energy is not used widely or much [1], but it is a very viable source of energy in the future, especially if it is used in innovative and eicient ways. Wind power has proven to be a growing industry, especially in the past seven years [2].
In this equation, represents velocity potential, U is the velocity of the moving sphere, a is the radius of the sphere, and (r, q) deines a polar coordinate determined by the location being studied. he following diagram depicts the scenario for this model:
he Physics of Wind Turbines Kinetic energy from moving air can be converted to usable electrical energy [3]. he method by which this can happen involves the rotation of blades on a turbine and the use of a generator. he rotation of the blades created by the lift forces of the wind moving causes the spinning of the rotor. he resulting circular motion induces changes in the magnetic lux within the generator, thus generating electrical current. Measurable factors dictate the power of a wind turbine [4], as expressed in the following function [5]:
Equation 1. In the above formula, P denotes power, r represents air density, C is the coeicient of performance (eiciency), A is rotor swept area, and n signiies wind speed. It is important to note that the wind speed component of the power formula is the wind speed that goes through the wind turbine. In the scenario proposed for this research project, that speed is not actually the same as that of the moving vehicle that is causing the blades of the wind turbine to spin. he speed of the wind through the wind turbine caused by a moving object can be calculated using the velocity potential ( ) of the luid ield surrounding the moving object [6]. In order to employ a relatively easy version of this
Â
Figure 1. he above diagram illustrates that the model suits a scenario in which both the turbine AND the sphereâ&#x20AC;&#x2122;s center fall on the same plane and the sphere moves at some velocity U. Volume 2 | 2012-2013 | 41
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In order to determine the luid/wind velocity at the given polar coordinate, the gradient of the velocity potential function must be taken. he resulting formula for wind velocity at the given polar coordinate after doing this gradient is:
Equation 2.
ď ś
Procedures To test the hypotheses presented, experimental roadside testing, a controlled single-car experiment, two calibration tests, and CFD tests were conducted. he primary materials required for the three non-CFD experimentation processes were four AL Turbine Complete Wind Turbine Kits (Model Number: A0012), 100-ohm resistors, LabPros/LabQuests, alligator clip wires, and differential voltage probes. he apparatuses were arranged on plywood bases and were staked into the ground. he following is a diagram of a single wind turbine apparatus:
In this equation, v represents the luid velocity vector at the given polar coordinate. his formula can be used to determine the wind velocity at any given coordinate relative to the moving automobile given the accompanying assumptions. For a spherical body with radius a moving to the left in a luid ield at a certain speed, U the luid velocity at a point with a coordinate (r, p/2) is the following:
Equation 3. he following is the result when typical values for the scenario being tested are substituted into this equation:
Â
he following is the result when the above value is substituted into Equation 1, in which typical values are also substituted:
he above value of .45W is a theoretically predicted value for an individual turbineâ&#x20AC;&#x2122;s power output in this research scenario. Combining (Equation 3) and (Equation 1) results in the complete theoretical power model, which is as follows:
Equation 4. Based on our research, it is hypothesized that if a miniature windmill is placed along the side of a road, then it will generate electricity from the wind draft of passing automobiles AND do so in accordance with the theoretical power output model derived above. 42 | 2012-2013 | Volume 2
Figure 2. his diagram demonstrates an individual apparatus from an aerial view. his apparatus allows for real time measurement of voltage data using Logger Pro measuring devices. One calibration test was conducted before and after the process of ield experimentation, to assure consistency among the wind turbine apparatuses. For the initial calibration test, each turbine apparatus was placed at a known distance in front of a box fan at two diferent speeds and voltage data was collected and analyzed to conirm consistency among the turbines. For the inal calibration test, the same procedure was followed except only one speed was used. Both times, the voltage output of all the turbines was found to be within 5-7% of the mean, thus showing that the turbines were well calibrated with respect to each other. For the experimental roadside testing, a location was found at which the average speed of cars was between 40 and 50 miles per hour and there was suicient space to place the turbines at the side of the road. At this location, the three experimental turbine apparatuses (deemed E1, E2, E3) were staked into a grassy path at the side of the road, with E1 closest to the road, the center of E2 thirty centimeters from the center of E1 (in the direction perpendicular to the road), and the center of E3 thirty centimeters from the center of E2. he control wind turbine (deemed C) was
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REsEaRch placed in a location that, in theory, would not be afected by traic wind. he experimental turbines were placed 2.8 meters away from each other in the direction of traic. Each rotor was angled thirty degrees toward the road from the parallel. An anemometer was staked into the ground thirty centimeters in front of E1, at the same elevation and distance from the road as E1. his experimental setup was implemented for an hour, during which voltage data and anemometer data were automatically taken. Traic data, which entailed the recording of traic pulse starting times and each pulseâ&#x20AC;&#x2122;s accompanying number of cars in the lane closest to the turbines, was also taken by hand/eye. hree such trials were conducted. he following are diagrams depicting the location and a single turbine: Â
model to perform tests on, so this was used to run various computational experiments. he following is an image of this object model:
Figure 5. In this diagram, one can see the image of a car in the luid ield, where the car is oriented in the picture such that its front is facing left.
he above object model involves a stationary car in a luid ield. In order to accurately model the research scenario, the luid was made to move at given speeds toward the car and boundary conditions were not set on either side of the car. his characterization of the research scenario accurately relects the experimental behavior because it merely involves a change of reference frame from the road to the moving car. here were three primary tests that were conducted on the car object model: a roadside wind speed model determination, an optimal velocity disturbance (roadside wind Figures 3 and 4. hese diagrams depict an overhead sche- speed) determination, and a comparison of experimental matic of the testing location and specs on an individual setup to optimized setup. wind turbine, respectively.
Data
he purpose of the controlled single-car testing was to generate a roadside turbine power output model based only on car speed and turbine distance. Five diferent car speeds were chosen for testing. he three experimental turbines were then tested at ive diferent distances for every speed (each speed was tested twice) and voltage data was taken for every run. A control turbine was also placed in an area so as to only be afected by ambient wind. he following table shows the set of distance and speed values that were tested:
Tables 1 and 2. he above tables display the ive distances (from the lane) and the ive speeds (of the car) that were tested in controlled single-car testing. Note that the intervals between test values are constant. Computational Fluid Dynamics software can be used to model real world scenarios involving luid dynamics. his is done by solving the Navier-Stokes equation for 3D object models with speciic parameters. AutoDesk CFD has a preloaded average-size car-in-a-luid-ield object
he irst of the following four graphs represents the power data obtained from each experimental turbine for each trial in the rush-hour roadside tests. he control data was omitted for reasons that will be discussed later in this paper. he second graph displays anemometer data and traic data for all three trials. he third following graph(Graph 3) shows the results of the controlled single-car testing.
Â
Graph 1. his is a graph that plots power of each experimental wind turbine vs. time. Note that the power spikes for each turbine occur at approximately the same times.
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REsEaRch view of the E1 voltage data from the irst trial as compared to the traic data: (on the following page)
Graph 2. his is a graph that plots wind speed vs. time
AND marks times that traic pulses occurred. Note that wind speed spikes tend to correspond with traic pulses.
Graph 4. he above graph displays a short interval that displays voltage and traic data. It is clear that there is a strong association between traic pulses and voltage spikes his graph also supports the correlation between traic and electricity generation within a range of timing uncertainty. his method was applied across all three trials and the same results were found, thus supporting the initial hypothesis of correlation.
Graph 3. his graph plots the power outputs of all of the turbines used in the controlled single-car testing vs. time. All of the data taken throughout the range of test values are displayed on this single graph.
Characteristic Analysis he characteristic analysis involved the statistical description of characteristic power spikes (CPS). CPS power values were determined by correlating the traic pulse timings to power spikes that were created by traic (same time as the traic pulses) and invoking the mean value theorem to determine average values for power for each CPS. hese power spikes were analyzed to directly study the efects of car-induced wind efects through the wind turbines. his method resulted in sets of data across the wind turbines and trials that represented power output during traic pulses. hese values were plotted in a bar-graph fashion as such:
Data Analysis he presented roadside testing data were analyzed in four diferent ways to get a multidimensional view of the results: correlation analysis, characteristic analysis, power model analysis, and error analysis. Each of these analysis methods will be discussed in detail in this section of the paper. Correlation Analysis he method of deining the correlation between road traic and increased turbine rotation followed from graphical comparisons between the traic pulse data taken during experimentation and the other forms of data (voltage and anemometer). he hand-written traic data was transposed to a graph and overlaid on the voltage and anemometer graphs to compare traic pulse timings to wind speed and voltage spikes, which would indicate a connection between the passing of automobiles and the increased generation of electricity. Graph 2 provides an example of this graphical comparison across all of the trials and it can be seen that the spikes in anemometer data can be directly attributed to traic pulses. he following is a narrowed 44 | 2012-2013 | Volume 2
Graph 5. he above graph displays one of many CPS data graphs. Each bar represents the average power value of its corresponding spike on the equivalent power data graph (Graph 1). All of the impertinent non-traic-pulse power information was removed before the creation of this CPS data graph.
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REsEaRch hese graphs were generated for all experimental wind turbines for all three trials, independently. he statistics operation available in LoggerPro was then used to determine some characteristics of these sets of data. he data displayed in the CPS power graphs were then used to produce frequency distributions. hese distributions were compared to the corresponding Gaussian distributions using the characteristic data. he following is a graph that displays a frequency distribution:
the distance relation of the power model. he data was then plotted in a log-log fashion, with one plot per trial, to easily deduce the power relation between power and distance and the constants associated with the power model. he following is an example of such a log-log plot:
Graph 7. he above plot displays the logarithm of Graph 6. he above graph displays a frequency distribution of one CPS data set. his graph and its associated Gaussian it show that the nature of traic patterns is highly unpredictable. hese analyses were conducted for all experimental data sets. Due to the unpredictable nature of the traic low, power outputs were expected to be highly varying. his theory is supported by the relatively large standard deviation values. Yet, it was found that on average the frequency distributions showed somewhat moderately good its with the Gaussian prediction, thus indicating that traic low was quasi-random. he following table depicts statistical results (that support the above observations) from the characteristic analysis of the data sets in Table 3. Power Model Analysis In order to compare the CPS data across the traic pulses, the E1 CPS power data was normalized to 1 and the other turbines’ data were scaled to follow the scaling of the E1 data. his produced a set of normalized power values that could then be plotted versus distance to analyze
normalized power values vs. the logarithm of distance values. he presence of a power-relation is quite apparent from the results of this plot.
In the above graph, log(N) represents the logarithm of the normalized power values discussed previously and log(r) represents the logarithm of the distance values. Using this plot and its accompanying linear regression, the power vs. distance relationship could be determined along with important constants expressed in the power and roadside wind speed models. hese values were found for all three trials and the averaged power law result is a power relationship of -1.3 +/- .3, with a predicted optimal power-it of –1.5. he predicted optimal power-it in conjunction with the log-log plot y-intercept information was then used to construct the accompanying models. he following are the two proposed optimal models in terms of theorized parameters:
Equation 5.
Equation 6.
Table 3. he above table displays all of important the statistical characteristics of the experimental roadside testing data. It is clear that the highly luctuating nature of traic low contributes to large standard deviation values.
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Street Broad Scientific he roadside wind speed model was used to scale down the anemometer data for E1 to the other two experimental turbines and thus overlay the theoretical power model values over the experimental values. An example of such an overlay over a small interval is the following:
Graph 8. he above overlay displays the raw data in blue and the power-model-predicted data in red. here is a visible time lapse due to timing diferences in anemometer data collection and E2 turbine data collection, but the moderately accurate predictive power of (Equation 5) and (Equation 6) is clearly apparent in the above graph, especially from the 28th minute to the 30th minute (when the time lapse is ignored).
REsEaRch It is apparent that the data taken from the control turbine were omitted from this study. his is because whenever large packets of traic would pass by the testing location, it was possible that the wind pulses could even afect the control. hus, as a result of unforeseen circumstances the control data needed to be omitted from the study. Yet, the correlation analysis still provides suicient evidence to show the inluence of automobiles on the wind turbines. he controlled single-car testing data was analyzed in two separate ways to generate a complete turbine power output model for the single-car scenario. he data were analyzed for a power-distance relation and a power-speed relation, as will be discussed in the following subsections. Controlled Single-Car Testing Analysis: Power-Distance Relation Analysis he way by which power relationships were determined for the controlled single-car testing data was virtually identical to the way by which they were determined in the power model analysis portion of the experimental roadside testing data analysis. he only diference was that each turbine was analyzed separately at a given speed and their results were compared to determine result validity. he following is a sample log-log plot for power-distance relation:
Error Analysis Error analysis entailed two major components, which were voltage uncertainty and power uncertainty. Yet, in order to obtain these uncertainties many other quantities had to be known. he formula for uncertainty in power was determined through the partial derivative method for absolute uncertainties. After utilizing this tool, the uncertainty in power was found to be:
he following is a table depicting pertinent uncertainties:
Graph 9. he above graph plots the logarithm of controlled single-car testing power values vs. three of the test distances. he strong linear relationship between the plotted variables is quite apparent in the above graph. he regression in the above graph had a slope of approximately –3, thus proposing a power-distance power relation of –3. his relationship was conirmed by the data from other speeds and turbines as well. hus, this section of the controlled single-car testing analysis produced the following result:
Table 4. he above table depicts the important uncertainties present in this study. It is clear that the uncertainties are fairly minimal, thus promoting conidence in the raw data values. 46 | 2012-2013 | Volume 2
Equation 7.
REsEaRch Controlled Single-Car Testing Analysis: Power-Speed Relation Analysis he power-speed relation analysis was conducted in the same manner as the power-distance relation analysis, except this time car speed was varied and the distance was held constant. Just as with the power-distance relation analysis, the turbines were analyzed separately at a given distance and then the results were compared. he following is a sample log-log plot for power-speed relation:
Street Broad Scientific ing for this computational experiment was incredibly similar to the controlled single-car testing procedures. An array of probe points was generated in the CFD model across the Z and X dimensions. he irst portion of the testing involved tests based on distance from the car, so at a given speed of 15 meters per second, the simulation was initiated and the steady state speeds at every point were determined. hese values were then averaged across the axis in the direction of the car. he second portion of the testing involved tests based on car speed, so one constant distance of 2.55 meters from the car was chosen and the simulation was run at a variety of speeds. he results for both tests were then plotted as such:
 Graph 10. he above graph plots the logarithm of con-
trolled single-car testing power values vs. three of the test car speeds. he strong linear relationship between the plotted variables is quite apparent in the above graph. he regression in the above graph had a slope of approximately 5, thus proposing a power-speed power relation of 5. his relationship was conirmed by the data from other speeds and turbines as well. hus, this section of the controlled single-car testing analysis produced the following result:
Graph 11. his graph shows a plot of the logarithm of wind speeds vs. the logarithm of the distance from the car. As can be seen, some of the distance values are within the boundary layer of the car (for the speed values increase in this region), but these were omitted for linear analysis.
Â
Equation 8. he two resulting power relations were combined to synthesize an experimental single-car turbine power output model. he model is as follows:
Equation 9. It can be noted that this model varies drastically from the experimental roadside testing power output model. his discrepancy along with the associated explanations and conjectures will be addressed in the Discussion section. CFD Analyses: Roadside Wind Speed Model Determination he purpose of this portion of the CFD analysis was to determine a model as a function of car speed and distance from the car for the wind speed generated from the roadside wind induced by the moving car. he method of test-
Â
Graph 12. his graph shows a plot of the logarithm of speed of interest (SOI, which is another term for roadside wind speed) vs. the logarithm of the car speed. As can be seen, the there is a strong positive linear association between the logarithms of the two plotted variables. Volume 2 | 2012-2013 | 47
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he following velocity dissipation model was generated as a result of the linear regressions done on the above scatterplots:
Equation 10. where some constant, car speed, and the distance from the car. he above model is incredibly similar to the roadside wind speed model generated from the experimental roadside testing:
where the real exponents have uncertainties of approximately .1. he intense similarities between the power relations in these two models indicate that wind speed could be approximated by traic low. To check the validity of the roadside wind speed model, the r-relations for the model were tested across a range of Y values, from .5 meter to 3 meters, for a range of X-values and a given Z value of 12 meters. To do this, an array of points on an XY plane were generated and the roadside wind speeds were probed and plotted for each point tested. After doing this, the same procedures for model determination as before were followed for each set of data grouped into the diferent Y-value sectors. For each of these blocked data sets, the r-relations were determined and the values for found to vary around a center (which was approximately 1 for the Z value of 12, which does not relect the results when using a range of Z-values as was done for the true model determination) by plus or minus .1. his shows that there is an inherent uncertainty of plus or minus .1 within the r-relation for the determined roadside wind speed model, which then furthers the argument of similarity between the experimental and CFD models due to intense overlap. CFD Analyses: Optimal Velocity Disturbance Determination he purpose of this portion of the CFD analysis was to determine the points of maximum wind speed, which would then indicate the optimal dimensions for a roadside wind turbine. In order to do this, wind speeds were measured at numerous points in every dimension after the simulation was run and then plotted to determine the maximum location in the X and Y dimensions. his is depicted in graphs 13 and 14. CFD Analyses: Comparison of Experimental Setup to Optimized Setup In the previous analysis, the location of maximum value was found to be (2.1 m, 1.7 m). he purpose of this analysis was to compare the power output of an optimized turbine 48 | 2012-2013 | Volume 2
Â
Graph 13. his graph shows a plot of the Z-velocity with respect to X-distance from the center of the car. he exact relationship between the two variables is diicult to determine from this plot, but the maximum values are easily identiiable.
Â
Graph 14. his graph shows a plot of the Z-velocity with respect to Y-distance from the bottom of the car. he relationship here can be seen to be quite smooth and predictable and the maximum values are easily identiiable. with dimensions to encompass this optimal location to the power output associated with the E2 turbine of the experimental roadside testing. he similarities of the velocity dissipation models between the CFD and roadside analyses shows that the CFD could approximate roadside events well, but yet another sub-test was conducted in order to further the legitimacy and validity of the CFD so as to allow a comparison of the experimental setup to the optimized setup. In order to do this, the simulation was run with the exact conditions of the roadside scenario. Table 6 summarizes these conditions.
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Table 5. he above table displays the experimental roadside testing conditions. Certain values out of these parameter speciications are certainly small and non-ideal and thus have room for improvement.
Table 6. he above table displays the optimal turbine/environment conditions. It can be seen here that the largest diferences between these parameter speciications and the experimental ones lie in the area value, the car speed value, and the Y coordinate value. he coordinate and car speed conditions were imposed upon the simulation and then the steady-state values across a range of Z-values determined to approximate the carefect time interval were recorded. hese values were used to calculate power outputs at every point and then were averaged across the speciic Z-axis. he resultant simulated E2 power output value was found to be .044 W. he actual average E2 power output (during traic pulses) was found to be .042 W in the experimental roadside tests. he closeness of these two values furthers the case for using the CFD model as an accurate approximation for roadside conditions. As such, the comparison of experimental to optimized setup could then be done and the results could have signiicant value. Following the simulation of E2 conditions, the CFD power output was known. he only remaining value necessary to conduct a comparison of experimental to optimized setup was the power output of the optimized setup. he process involved in inding this was the exact same as for the simulation of E2 except with the following parameters outlined in Table 6. he imposition of these parameters generated a Z-averaged power output of 122 W. he ratio of this power output (optimized setup) to that of the experimental setup is 2782.
As such, the mere implementation of a new turbine design to achieve the optimal parameters, which are within reasonable bounds, could theoretically increase the power output observed through the experimental roadside tests almost 3000-fold.
Discussion he primary objectives of this research were to determine whether the proposed idea of using miniature wind turbines on the sides of roads would generate electricity from the wind draft of cars and to test the theoretical power output model generated for this scenario. It was hypothesized that the idea would generate electricity and the power output model would it the data, but the data and its following analysis showed results of a diferent nature. he irst prong of the hypothesis was undoubtedly supported by the data as was shown in the correlation analysis. he second prong of the hypothesis was not supported, but elements of the power model associated with it followed through to the inal proposed models. he initial power model was the following:
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Street Broad Scientific here is deinitely a high degree of uncertainty in the distance relation due to the turbulent nature of the scenario and random nature of traic low, but this optimal proposed model its experimental power data very accurately and the power relations are supported by the CFD testing. he primary reason for the invalidity of the theoretical model is probably the inapplicable assumption of laminar low in the theoretical roadside wind speed model. Although eforts were taken to produce as high quality data as was possible, there are certainly areas of potential improvement. he turbine design could have been slightly more aerodynamic, the generators could have been slightly more eicient, the data set timings could have been better synchronized, and the data collection tools could have been more precise. Furthermore, there were certainly sources of error within the process of experimentation such as the natural imprecision of the testing equipment, slight inconsistencies in turbine yaw, and potentially imprecise distance measurements. hese errors, compounded with the unpredictable nature of turbulent wind and traic, are expressed as higher uncertainties and lower conidence levels in the results. he controlled single-car testing analyses produced a power output model that disagreed with both the theoretical model AND the roadside model. he following is the controlled single-car testing power output model:
where k is a constant. his discrepancy between roadside large-scale traic power output and single-car power output led us to the conjecture that there must also be an additional traic parameter involved in a holistic turbine power output model, which may afect the power-distance and power-speed relations. Research into this traic-parameter-based holistic turbine power output model is an intriguing potential topic of future study. Along with research into the potential traic parameter component of a holistic turbine power output model, there are many other possible ofshoot branches from this experiment that could be studied as future work. One such possibility is the study of the roadside “funneling efect”. his efect was observed during experimentation to be the compounding of wind in abnormally high-speed gusts at the side of the road whenever large packets of relatively fast moving automobiles passed. Another possible topic of future interest is Computational Fluid Dynamics modeling of the aerodynamic scenario we studied in this research project. Yet another possibility for future work could involve the studying of turbine design and roadside turbine arrays to potentially achieve the theoretical optimal power values described in the CFD analysis section. All of these potential topics of future study are key components of the remaining steps in furthering this concept of roadside electricity 50 | 2012-2013 | Volume 2
REsEaRch generation to make it economically feasible. At this point in time, based on our research, the idea is certainly not feasible. Yet, without a doubt there is a possibility that improvements could be made that could allow this idea to become the next innovative foray into the ield of renewable energy.
Acknowledgements I would like to thank Dr. Bennett and Mr. Milbourne, my mentors. I would also like to thank the Research in Physics program and the NCSSM Board of Trustees for allowing me to pursue this fantastic opportunity.
References [1] “Primary Energy Sources â Fuels at the Heart of the Matter.” Classroom Energy. N.p., n.d. Web. 08 Feb. 2012. <http://www.classroom-energy.org/energy_09/3.html>. [2] “Wind Energy Companies.” Wind Energy Companies. Web. 09 Feb. 2012. <http://www.greenchipstocks.com/articles/wind-energy-companies/273>. [3] “Wind Energy Basics.” Wind Energy Basics. N.p., n.d. Web. 12 Feb. 2012. <http://windeis.anl.gov/guid/basics/ indes.cfm>. [4] Constantino, D. “Winning with Wind.” Pit & Quarry (2008): 2. Web. 13 Jan. 2012. [5] Kovarik, homas J., Charles Pipher, and John A. Hurst. Wind Energy. Northbrook, IL: Domus, 1979. Print. [6] Batchelor, George K. “6.8.” An Introduction to Fluid Dynamics. Cambridge [u.a.: Cambridge Univ., 2009. Print.
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Shocking Discoveries: The Applications and Putative Mechanisms of the Effects of Electric and Magnetic Fields on Plants Ian Maynor “Life and death appeared to me ideal bounds, which I should irst break through, and pour a torrent of light into our dark world.” - Dr. Victor Frankenstein, Frankenstein by Mary Shelley
Introduction Images of Frankenstein’s creation of life through the powerful force of electricity can be found everywhere throughout popular culture. his ubiquitous motif relects a human fascination with the seemingly supernatural properties of electromagnetism, a power considered so great that we have often imagined it can achieve the impossible; even bring the dead back to life. he study of electromagnetic energy in living things, bioelectromagnetism, began in the late 1700s when the Italian scientist Luigi Galvani discovered that applying static electricity to frog legs caused them to move. Since then, iction has used the imagined power of electromagnetism to create villains such as Frankenstein’s monster and in the powers of superheroes such as Magneto or Storm. Yet perhaps just as shocking are the real-life applications of bioelectromagnetism, speciically in its potential use in plants and agriculture to improve plant growth, yield, germination rate, and nutrition. Even simply exposing plant seeds and irrigation water to diferent types of electromagnetic energy have proven to achieve similar efects in a wide variety of plants and even livestock [1]. hese results suggest that the exposure of plants to magnetic and electrostatic ields could provide an ecologically friendly, afordable way to increase crop production without polluting the soil with chemical fertilizers [2]. here seem to be few, if any, drawbacks to these novel approaches; a review paper on the potential genotoxicity of electric and magnetic ields dismissed claims in 34 studies that extremely low frequency electric and magnetic ields could harm plant genomes [3]. here are still, however, many issues to resolve before we can approach a comprehensive understanding of various types of electromagnetic energy on crops and eventually apply this knowledge on a large scale: i.e., which methods of applying electromagnetic energy best improve plant growth and yield, whether these methods can increase nutrition and productivity as well as growth, and most importantly, what the causes behind these astonishing indings are.
Varying Methods of Applying Electric and Magnetic Fields to Plants In 1930, the Russian researcher Savostin conducted one of the irst studies on the efects of magnetic ields on seeds when he observed increased oxidation rates in wheat seedlings under magnetic conditions [4]. Over a decade later, researchers observed changes in seed germination due to magnetic ields [5]. Since then, researchers have tested a wide variety of methods of exposing plants to electric or magnetic ields to improve their germination and growth. Between electric and magnetic ields, there is a wide array of diferent techniques of applying electromagnetic energy to plants in hopes of improving their growth and yield. Electric ields are forces formed by a diference between static charges, while magnetic ields are forces created by moving charges. Although these forces are diferent, researchers have observed strikingly similar results in plants whether using electric or magnetic ields. Some researchers have achieved improvements in plant growth and yield by exposing plants to magnetic ields while they grow. Naturally, all plants grow in the presence of magnetic ields, as the earth’s magnetism creates a global magnetic ield. However, by applying magnetic ields beyond the local geomagnetic ield, researchers have achieved impressive results in improving growth and yield. Novitsky et al. grew green and bulb onions under horizontal permanent magnetic ields of strengths around 43 A/m produced by Helmholtz coils (Figure 1) and observed increased bulb sprouting in both green and bulb onion [6]. he same study also found that the magnetic ields accelerated sprouting in both types of onions, which the study attributed to cell elongation; however, this stimulating efect of permanent magnetic ields only lasted in the sprouting stage of the onion’s growth [6]. he magnetic ields were also found to increase plant yield by increasing the number of sprouts in green onions and the number of sprout bunches in bulb onions; this focused growth suggested that permanent magnetic ields enhance the genetically determined growth patterns seen in untreated onions [6]. Permanent alternating magnetic ields, created by continuous, alternating electric currents, have also Volume 2 | 2012-2013 | 51
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Methods of Exposing Plants to Magnetic Fields
Figure 1. Helmholtz coils
Figure 3. Helmholtz coils Figure 5.
Figure 2. Figure 4. been shown to improve plant growth and yield. EĹ&#x;itken and Turan grew strawberry plants beneath electric wires through which an alternating current was passed, creating an alternating magnetic ield which magnetically treated the plants beneath the wires (Figure 3) [7]. hey found that weaker magnetic ields (0.096 T) increased fruit yield, fruit number per plant, and average fruit weight in strawberries compared to the control and treatments with stronger ields [7], showing that continuous exposure to alternating magnetic ields can increase plant yield just as continuous exposure to nonalternating ields has [6]. Similar results have been achieved in a number of studies on the efects of pre-sowing magnetic treatments of plant seeds in a variety of plants. In such studies, seeds are exposed to magnetic or electric ields before sowing, and the efects on growth and yield are observed. Some of these studies used Helmholtz coils (Figure 4) to expose seeds to magnetic ields ranging in strength from 0 to 10 mT [2, 8, 9]; others used diferent methods to magnetically treat seeds, such as placing the seeds between two bar electromagnets [10]. Researchers have observed that pre-sowing treatment with pulsed magnetic ields improved plant growth and yield [8], while pre-sowing magnetic treatments on tomato seeds have been found to signiicantly improve percent germination rates, plant growth, yield, and fruit size [11]. In contrast, electric ields with greater intensities and certain exposure times were found to inhibit germination in tomato seeds [10, 11]. Even more interesting, however, magnetic treatments ranging from 100 to 170 mT and 3 to 10 minutes were found to signiicantly delay the onset of geminivirus and early blight in tomatoes and were observed to cause a reduced infection rate of early blight [10]. Similar efects, such as increased germination and yield, have been repeated in the nonfood crop cotton as well [2]. However, as seen throughout all of these studies, the efects of magnetic ields on plants difer from one species to the next and even between varieties of the same species [2].
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Stationary magnetic ields have also been demonstrated to increase the germination rate and growth of plants. he stationary magnetic ields used in such experiments are produced by the permanent magnets found in everyday life. Researchers have found that exposing corn seeds to stationary magnetic ields at various strengths increased their height and weight as seedlings, with signiicant improvements observed when the seeds were exposed for 24 hours or more at magnetic ield strengths of 125 mT and 250 mT [12]. As a reference, kitchen magnets have a strength of around 5 mT, sunspots a strength of about 150 mT, and loudspeaker magnets a strength of around 1 T. Similar studies have shown that while magnetic ields improve plant germination rate, the ields do not afect the total amount of germination under laboratory conditions (as opposed to ield conditions), as the increased germination rate only allowed the plants to reach the same saturated amount of germination at a faster pace than the control [11, 12]. However, magnetic ield exposure has been shown to increase the total number of germinated seeds under ield conditions [2]. Yet another approach to increasing germination in plants involves subjecting seeds to high voltage electric ields (HVEF) by placing them between metal plates charged with high voltages, producing electric intensities equal to 450 kV per meter of space between the two plates. HVEF have been found to increase the germination rate and total germination of aged wet rice seeds and signiicantly increase their vigor [13, 14].
Figure 6. Even more intriguing studies have observed that magnetic treatment of irrigation water can improve crop and livestock yield [1, 15]. Studies such as these run water through magnetic treatment devices, in which water is run through a pipe positioned between two magnets [15]. Fas-
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REviEw cinatingly, similar efects are found in livestock as well. Lin and Yotvat tested the efects of magnetically treated irrigation and drinking water on cows, geese, sheep, turkeys, and melons. Treated cows were found to produce more milk, be more fertile, and grow faster than the control group, while treated geese and turkeys were heavier than the controls [1]. Treated sheep’s milk, meat, and wool yield were all increased [1]. Lin and Yotvat concluded that these data purport the beneits of using magnetically treated water on an agricultural scale, including applications in ish farming, algae, produce, and livestock, using electromagnetic units for treating water which are already commercially available [1].
nisms which could then be applied to a larger number of crops and methods to pave a path for the potential largescale agricultural use of bioelectromagnetics in the future.
Efects on Nutrition and Productivity Some of the studies on the abilities of magnetic ields to improve crop yield have also found that magnetic ields increase certain nutritional values of plants. Novitsky et al. observed that magnetically treated onions contained larger amounts of chlorophyll and protein, but not carbohydrates, in comparison to the control [6]. Lin and Yotvat found the magnetic treatment of water increased the sugar content of melons and made the meat of treated cows leaner compared to controls [1]. However, few studies have studied the efects of magnetic ields on plant nutrition besides studies focusing on nutrient uptake as a potential explanation for increased germination and growth in magnetically treated plants. Additional research is needed to explore the ability of magnetic ields to increase the nutritional values of various plants, which holds the promise of producing not only higher quantity but also higher quality plants.
he Mechanisms behind the Data Figure 7. he magnetic treatment of water has also been suggested to cause treated plants to use water more eiciently. Maheshwari and Grewal found that celery irrigated with magnetically treated water experienced signiicant increases in productivity (weight produced per volume of water used) [15]. Although the total amount of water used for the treated plants was the same as the control, the increased yield induced by the magnetic ields accounted for the increase in water productivity [15]. However, few, if any, other studies have investigated the efects of magnetic ields on water productivity; further studies must be done in this area to either support or counter claims that magnetic ields may generally increase water productivity. If proven, increased water productivity in plants under magnetic treatments could add to the already sizable number of beneits magnetic ields lend to plants and would be especially useful for farmers growing crops in dry regions. As can be seen, a large number of studies have used electromagnetic energy to increase germination, growth, and yield in plants through a variety of methods: continuous exposure to magnetic ields, seed exposure to magnetic ields, seed exposure to high voltage electrostatic ields, and irrigation and drinking water exposure to magnetic ields. However, no studies have compared any of these methods side by side to see which is most efective in improving plant germination, growth, and yield. Little research has been done on how diferent plants are afected using the same method of exposing plants to a certain type of electric or magnetic ield. While researchers have found many speciic beneits of electric and magnetic ields on plants, broader studies should be conducted to ind basic mecha-
While many studies have tested the abilities of magnetic ields to improve plant growth and yield, few have investigated the causes of these strange phenomena. Many papers have noted increases in nutrient uptake in plants treated with magnetic ields [7, 8, 11]. Eşitken and Turan attributed magnetically treated plants’ selective uptake of positive ions over negative ions to the negative electric charge of plant cells [7, 16], which cause them to uptake positively charged ions. hese results suggest that magnetic ields may increase the negative charge of plant cells, thereby increasing their uptake of positive ions, many of which are nutritional to plants and may help improve a plant’s growth and yield. Moon and Chung observed that external electric and magnetic ields inluence ion activation and dipole polarization in living cells, providing a possible explanation for the increase in the plants’ nutrient uptake due to magnetic ield exposure [11]. Another study proposed that the magnetic treatment of plants electromagnetically induced change in the electrostatic balance of plant systems at the cell membrane level, which is the main site of any inhibition or enhancement of plant growth [2]. It has also been proposed that magnetic ields could afect the transport of charged solutes into the cell by the activity of enzymes controlling the local extensions of plant cell [2]. he correlation between mineral uptake and magnetic ields’ positive efects on plants suggests that former may also be one of the factors responsible for the unique efects of magnetic ields on plants. It has been proposed that magnetically treated plants release organic compounds into the rhizosphere, the soil surrounding the plant, which may increase P and K desorption, the release of phosphorus and potassium through or from a surface; these elements would therefore become more available to Volume 2 | 2012-2013 | 53
Street Broad Scientific the plant, aiding plant growth [15]. Results from the same study have suggested that magnetically treated water also improved availability, uptake, assimilation, and mobilization of these nutrients within the plant system, providing a possible explanation for the treated plants’ increased water productivity when compared to the control [15]. Other evidence suggest that the positive efects of magnetic treatments on plants may result from a reduced rather than increased accumulation of certain minerals; Maheshwari and Grewal proposed that the magnetic treatment of water may inhibit the plants uptake of Na, thereby decreasing Na toxicity [15], which other studies have found to limit plant growth [17, 18,19]. Another possible explanation for ability of magnetic ields to enhance plant germination, growth, and yield is via the water relations within plants. One study found results suggesting that stationary magnetic ields change the mechanism of water uptake in lettuce seeds, allowing them to absorb more water [9]. Citing a paper in which increased osmotic pressure and therefore increased water uptake were suggested to stimulate cell growth rates [20], the researchers concluded that the correlation between water uptake and magnetic ields they found may be responsible for the increased, magnetically induced germination rates found in other studies [9]. Another study proposed that non-uniform magnetic ields may energetically excite one or more parameters of the cellular substratum, such as proteins and carbohydrates, or water within seeds; after these magnetically exposed seeds acquired water, the activation and production of enzymes and hormones would be enhanced due to initial stimulation from magnetic ields, which could lead to improved plant germination, growth, and yield [10]. Other studies found that magnetic treatments increase protein synthesis and content in plant cells [6, 8]. However, since little research has been done on the relationships between magnetic ields and water uptake, cellular substratum stimulation, and photosynthesis, these three plausible explanations for the efects of magnetic ields on plants must be more thoroughly researched to reach informed conclusions on the true causes of magnetic ields’ special efects on plants. Several studies have also pointed to enzymes as possible factors in magnetic ields’ efects on plants by linking electric and magnetic ield exposure to enzyme activity [8, 21]. Radhakrishnan and Kumari proposed that magnetic ields afect the photosynthetic enzyme Rubisco subunits, which are largely responsible for carbon ixation in plants and therefore lead to enhanced carbon ixation and growth [8]. Pulsed magnetic ields were also found to increase activity of catalase (an enzyme which catalyzes the decomposition of hydrogen peroxide to water and oxygen) in soybean seedlings, suggesting that magnetic ield treatment leads to increased catalase activity and therefore greater decomposition of harmful reactive oxygen species; moreover, the formation of water may have in turn enhanced plant growth [8]. Nazar et al. concluded that further research should be conducted regarding whether the bio54 | 2012-2013 | Volume 2
REviEw logical efects of electric and magnetic ields is ield or cell speciic [21]. Another study claims that increased growth and yield in plants exposed to electric or magnetic ields could be due to an increased activity of enzymes that decompose harmful reactive oxygen species [14]. Wang et al. additionally proposed that magnetic treatments increase lipid peroxidation of harmful reactive oxygen species and that increased enzyme activity due to electrostatic ield treatment leads to greater lipid peroxidation, less damage to seedlings, and therefore more growth in seedlings [14]. Furthermore, enzyme activity may be determined by a factor previously discussed, ion accumulation: one study found that metal ions can inhibit acid phosphatase to varying degrees in corn and soybean roots, suggesting a link between ion concentration and enzyme activity [22]. his could mean that the increased ion concentrations caused by electric or magnetic ield exposure may be responsible for increased or decreased enzyme activity. Lin and Yotvat found that the efects of treating irrigation and drinking water depended on the type of water, water content, temperature, equipment, equipment location, and operational factors such as water volume, speed of low, installation, maintenance, etc., [1] but provided no explanation for the increases in growth, yield, and nutrient content in magnetically treated crops and livestock observed in their study. A possible explanation for these results is that magnetic ields alter some element of water which makes them more functional within the plant system and probably afects plant growth at the cellular level [15]. Recent studies have found that magnetic ields do, in fact, change some chemical and physical characteristics of water [23, 24], and these efects have been observed to last long after the magnetic ield is removed [25]. Researchers have discovered that magnetic ields increase the size of water molecule clusters bound by hydrogen bonds in liquid water [24]. his relates to indings that magnetic ields increase the strength of hydrogen bonds in water [26]; this correlates to a weakening of van der Waals forces in water, since the delicate balance between conlicting hydrogen bonding and non-hydrogen-bonding forces in water clusters means that stronger hydrogen bonding will accompany weaker van der Waals forces [26]. Researchers have proposed that magnetic ields create dampening forces which reduce the thermal motion of charges inherent in water, strengthening the hydrogen bonding between water molecules [27]. hey also found that magnetic ields increase the rate of evaporation by decreasing the strength of van der Waals forces and increase water’s boiling point, supposedly due to increased hydrogen bonding [27]. By increasing the strength of certain bonds within water, magnetic ields could possibly afect the transpiration and uptake of magnetically treated water in plants, ofering a possible explanation for the ability of magnetically treated water to increase water productivity. Strong magnetic ields have been found to enhance salt mobility as well [28], which could account for the greater ion concentra-
REviEw tions found by Maheshwari and Grewal that may be responsible for the efects of magnetic ields on plants [15]. Magnetic ields also have the ability to increase proton spin relaxation, which may quicken some proton-transfer dependent reactions [29], which may help explain the increased enzyme activity proposed by Wang et al. to help promote seedling growth [14]. Many studies concede that researchers still do not know enough about the mechanisms causing magnetic and electric ields to increase germination, growth, and yield in plants. Comprehensive studies must test a variety of possible factors to explain this behavior at a cellular level so that broader generalizations on the mechanisms of these phenomena can be reached. Once they are determined, we will be able to better understand if, when, and how electric and magnetic ields can improve plant germination, growth, and yield and determine ways to maximize these beneits.
Conclusion Much like the observers of Galvini’s experiments, we are currently testing the efects of electromagnetic energy on a wide variety of organisms, seeing if this powerful force can increase the germination, growth, and ultimately yield of both plants and animals. Yet, similar to the men and women of Galvini’s day, we are still unsure of the exact science behind the radical results we see. If we truly want to unlock the potential of electric and magnetic waves in maximizing plant and livestock production, to break the “ideal bounds” Dr. Frankenstein referred to, we must irst deinitively determine the causes of these strange phenomena. In conclusion, there remains much to be probed and discovered in the study of electric and magnetic ields’ effects on plants and livestock. First, we must determine the general causes of the peculiar behavior of plants exposed to magnetic or electric ields, such as their tendency toward increased germination, growth, yield, productivity, and nutrition. After establishing a irm and broad conceptual basis, which we now lack, we can then conirm or disprove claims that magnetic ields can increase productivity and nutrition and additionally determine how to manipulate the factors behind this amazing behavior to maximize the beneits of electric and magnetic ields on plants. From there, we can then commercialize this process for wide-scale agricultural applications which could meet the constantly growing demand for food in an ever expanding world.
References [1] Lin, I.J., and J. Yotvat. 2002. Exposure of irrigation and drinking water to a magnetic ield with controlled power and direction. Journal of Magnetism and Magnetic Materials 83: 525-526.
Street Broad Scientific [2] Leelapriya, T., K.S. Dhilip, and P.V. Sanker Narayan. 2003. Efect of weak sinusoidal magnetic ield on germination and yield of cotton (Gossypium spp.). Electromagnetic Biology and Medicine 22: 117-125. [3] McCann, J., F. Dietrich, and C. Raferty. 1998. he genotoxic potential of electric and magnetic ields: an update. Mutation Research/ Reviews in Mutation Research 411: 45-86. [4] Savostin, P.W. 1930. Magnetic growth relations in plants. Planta 12, 327. [5] Murphy, J.D. 1942. he inluence of magnetic ield on seed germination. Am. J. Bot. 29(Suppl.), 15. [6] Novitsky, Y.I., G.V. Novitskaya, T.K. Kocheshkova, G.A. Nechiporenko, and M.V. Dobrovol’skii. 2000. Growth of green onions in a weak permanent magnetic ield. Russian Journal of Plant Physiology 48: 709-715. [7] Eşitken, A., and M. Turan. 2004. Alternating magnetic ield efects on yield and plant nutrient element composition of strawberry (Fragari ananassa cv. Camarosa). ActaAgric. Scand., Sect. B, Soil and Plant Sci. 54: 134-139, 2004. [8] Radhakrishnan, R. and B.D.R. Kumari. 2012. Pulsed magnetic ield: A contemporary approach ofers to enhance plant growth and yield of soybean. Plant Physiology and Biochemistry 51: 139-144. [9] Reina, F.G., L.A. Pascual, and I.A. Fundora. 2001. Inluence of a stationary magnetic ield on water relations in lettuce seeds. Part II experimental results. Biolectromagnetics 22: 596-602. [10] De Souza, A., D. Garcia, L. Sueiro, F. Gilart, E. Porras, and L. Licea. 2006. Pre-sowing magnetic treatments of tomato seeds increase the growth and yield of plants. Bioelectromagnetics 27: 247-257. [11] Moon, J.D., and H.W. Chung. 2000. Acceleration of germination of tomato seed by applying AC electric and magnetic ields. Journal of Electrostatics 48: 103-114. [12] Flórez, M., M.V. Carbonell, and E. Martínez. 2007. Exposure of maize seeds to stationary magnetic ields: effects on germination and early growth. Environmental and Experimental Botany 49: 68-75. [13] Wang G., J. Huang, W. Gao, J. Li, R. Liao, and C.A. Jaleel. 2009a. Inluence of high voltage electrostatic ield (HVEF) on vigour of aged rice (Oryza sativa L.) seeds. Journal of Phytology 2009, 1: 397-403. [14] Wang G., J. Huang, W. Gao, J. Lu, J. Li, R. Liao, C.A. Jaleel. 2009b. he efect of high-voltage electrostatic ield (HVEF) on aged rice (Oryza sativa L.) sees vigor and lipid peroxidation of seedlings. Journal of Electrostatics 67: 749-764. [15] Maheshwari, B.L., and H.S. Grewal. 2009. Magnetic treatment of irrigation water: its efects on vegetable crop yield and water productivity. Agricultural Water Management 96: 1229-1236. [16] Marschner, H. 1995. Mineral nutrition of higher plants. Academic Press Limited. 24-28 Oval Road, LonVolume 2 | 2012-2013 | 55
Street Broad Scientific don NW1 7DX, 889 pp. [17] Franรงois. L.E., T.J. Donovan, E.V. Maas, and S.M. Lesch. 1994. Time of salt stress afects growth and yield components of irrigated wheat. Agron. J. 86: 100-107. [18] Munns, R. 2002. Comparative physiology of salt and water stress. Plant Cell Environ. 25: 239-250. [19] Muranaka, S., K. Shimizu, and M. Kato. 2002. Ionic and oxmotic efects of salinity on single-leaf photosynthesis in two wheat cultivars with diferent drought tolerance. Photosynthetica 40: 201-207. [20] Cosgrove D. 1993. Water uptake by growing cells an assessment of the controlling roles of wall relaxation, solute uptake, and hydraulic conductance. Int. J. Plant Sci. 154: 10-21. [21] Nazar, A.S.M.I., A. Paul, and S.K. Dutta. 1996. Frequency-dependent alteration of enolase activity by ELF ields. Bioelectrochemistry and Bioenergetics 39: 259-262. [22] Juma, N.G., and M.A. Tabatabai. 1988. Phosphatase activity in corn and soybean roots: conditions for assay and efects of metals. Plant and Soil 107: 39-47. [23] Pang, X.F. and B. Deng. 2008. Investigation of changes in properties of water under the action of a magnetic ield. Science in China Series G-Physics Mechanics Astron. 51: 1621-1632. [24] Cai, R., H. Yang, J. He, W. Zhu. 2009. he efects of magnetic ields on water molecular hydrogen bonds. J. Mol. Struct. 938: 15-19. [25] Pang, X. and B. Deng. 2010. Infrared absorption spectra of pure and magnetized water at elevated temperature. Europhys. Lett. 92: 65001. [26] Hosoda, H., H. Mori, N. Sogoshi, A. Nagasawa, and S. Nakabayashi. 2004. Refractive indices of water and aqueous electrolyte solutions under high magnetic ields. J. Phys. Chem. A. 108: 1461-1464 [27] Inaba, H., T. Saitou, K. Tozaki, and H. Hayashi. 2004. Efect of the magnetic ield on the melting transition of H2O and D2O measured by a high resolution and supersensitive diferential scanning calorimeter. J. Appl. Phys. 96: 6127-6132. [28] Chang, K.-T. and C.-I. Weng. 2008. An investigation into the structure of aqueous NaCl electrolyte solutions under magnetic ields. Comput. Mat. Sci. 43: 1048-1055. [29] Madsen, H.E.L. 2004. Crystallization of calcium carbonate in magnetic ield in ordinary and heavy water. J. Cryst. Growth 267: 251-255.
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Halobacterium: Mechanisms of Extreme Survival as a Solution to Waste Isaiah Stackleather Introduction Halobacteria is a class of archaebacteria that thrive in harsh environments with a unique capability to survive in hypersaline environments. Research conducted today shows that halobacteria diplay unique environmental response capabilities not only to high concentrations of salt, but also to desiccation, gamma irradiation, oxidative stress (the scarcity or overabundance of oxygen species such as O2 and H2O2), and microgravity [1,2,3]. In short, halobacteria are unique archaebacteria whose characteristic ability to survive in extreme environments is unlike that seen in most other microorganisms. his review aims to describe physiological mechanisms that halobacteria utilize survive and how these responses may provide solutions in ields such as waste management.
Environmental Stress Response Characterization Halobacteria can survive in many hypersaline environments. However, when it comes to environments of oxidative stress, microgravity, or gamma irradiation, little is known about their phenotypic response. he following section gives insight into the characteristic responses when exposed to certain extreme conditions. Desiccation Given the halobacteriaâ&#x20AC;&#x2122;s known response to hypersaline systems, Kottemann et al. explored whether the wild-type halobacteria strain NRC-1 would respond in a similar way to desiccation, and the bacteria were, indeed, able to withstand high levels of desiccation. When placed in desiccation for twenty days, there was a 25% survival of viable cells with an almost full DNA recovery in two days [1]. herefore, Kottemann concluded that this ability to survive high levels of dryness occurred because of its capability to quickly repair double-stranded DNA breaks. hese results conirm the conclusions of Malcolm Potts, who suggested that halobacteria must either adapt to desiccation or already possess the capability to resist its harmful efects. NRC-1 colonies took refuge within salt crystals in order to protect themselves against desiccation [1]. his behavior, typical of haloarchaea, has even been observed in the viable mitochondrial DNA of haloarchaea found in 60,000 year old bones of deceased Aboriginals, demonstrating the efectiveness of this response [4]. Kixmuller et al. ofers an explanation for halobacteria survival within halite crystal deposits. Halobacteria thrive in environments with high concentrations of potassium
(K+) ions. However, desiccated environments, such as deserts, do not maintain very high K+ ion concentrations. heir survival is possible because of the kdpFABCQ operon, which regulates the expression of a gene that codes for the production of K+ ions, allowing the survival of halobacteria [5]. In non-desiccated regions, the function of this operon is not necessary. A knockout strain of the wild-type halobacteria, whose kdpFABCQ operon was nonfunctional, was exposed to a desiccated environment. he strain yielded a viable cell count 110 times less than that of the wild-type at the end of the desiccation period, conirming that in order for halobacteria to survive within halite crystals, the kdpFABCQ operon must be completely functional in order to create enough K+ ions for cell survival. Gamma Irradiation Kottemann et al. exposed halobacteria wild-type NRC-1 to high levels of gamma irradiation (up to 7.5 kGy, many times more than humans can withstand). In the presence of high gamma radiation, as also seen with desiccation exposure, wild-type NRC-1 experienced double-stranded breaks in their DNA, which were repaired after 48 hours of exposure. When subjected to the highest level of gamma radiation (7.5 kGy), NRC-1 did not yield a viable cell count. However, even with medium levels of gamma radiation exposure (2.5-4 kGy), NRC-1 managed to show resilience and maintained a 25% viable cell count [1]. From these results, Kottemann concluded that desiccation resistance and gamma irradiation resistance are related since NRC-1 can repair breaks in DNA caused by both stressors very eiciently. Furthermore, the natural pigmentation of NRC-1 and its habit of hiding within salt crystals, as seen previously, allows the bacteria extra protection from gamma irradiation. his experiment ofers insight into resistance of halobacteria to high levels of gamma irradiation. Oxidative Stress Oxidative stress, or an overabundance of toxic oxygen species, is another extreme environment in which halobacteria have been demonstrated to survive. Sharma et al. conducted an experiment on halobacteria wild-type NRC1, dealing with the transcription factor VNG0258H. As the concentration of reactive oxygen species within the surrounding environment increased, the expression of VNG0258H increased, allowing NRC-1 to survive oxidative stress. When the concentration of reactive oxygen speVolume 2 | 2012-2013 | 57
Street Broad Scientific cies was decreased, the level of expression of VNG0258H decreased as well, as is illustrated in Figure 1 [2].
REviEw growth. Researchers found that 1.5 M NaCl, 35° C, and a pH of 8.0 were the optimal conditions for haloarchaea in the removal of chromium waste, for it was at these levels that the maximum chromate removal was achieved, resulting in a inal concentration of chromate ions well below 0.04 mM. herefore, halophiles are very applicable to the ield of biohazard waste management, especially in regards to chromium waste.
Conclusion
Figure 1. his igure shows the relationship between oxygen species level over time and the expression of VNG0258H and aerobic and anaerobic genes. In both cases of high oxygen levels, VNG0258H had the highest levels of regulatory gene expression. From this, Sharma concluded that there was a relationship between VNG258H expression and NRC-1 resistance to oxidative stress. For further conirmation, Sharma created a knockout strain of NRC-1, without functioning VNG0258H transcription factors, and placed it in varying levels of oxidative species concentration. As reactive oxygen species concentration increased, the survival of the knockout bacteria decreased, conirming the necessity of VNG0258H in the regulation of oxidative stress. Microgravity Dronmayr-Pfafenhuemer et al. explored the response of halobacteria to simulated microgravity (gravitational force that is 100 times weaker than that of Earth’s) [3]. he survival of of the halobacteria species, Haloferax mediterranei, was tested in simulated microgravity with exposure to antibiotics. When placed in simulated microgravity, Haloferax mediterranei maintained a reasonable cell density after 6 days of exposure to the antibiotics. However, when exposed to normal Earth gravity, Haloferax mediterranei was only able to survive for a maximum of approximately 48 hours when exposed to the antibiotics. herefore, Dronmayr-Pfafenhuemer concluded that, when subjected to microgravity, the resistance of halobacteria to antibiotics and other environmental stresses increases.
Waste Management In the future, humans could take advantage of the ability of halobacteria to survive extreme environments, particularly in their waste removal capabilities. Amoozegar et al. explored the application of the response of a particular haloarchaea strain to toxic chromium waste [6]. Chromium creates very toxic saline waste, and since the waste has a high salt concentration, it is ideal for haloarchaea 58 | 2012-2013 | Volume 2
In conclusion, halobacteria are very complex organisms that are able to survive a wide range of environmental stressors, such as desiccation, high gamma irradiation, and microgravity. hese versatile organisms have a variety of mechanisms with which it adapts to stressful biological environments. Not only can these bacteria withstand extreme conditions, but their response mechanisms could also allow for their use in waste management. Further research includes the application of halobacteria to desiccated environments characterized by subzero temperatures, as well as environments not present on Earth, as seen on other planets. It is likely that other unique biological responses of halobacteria will be discovered and, therefore, may provide an untapped natural resource that could be put to work to beneit our society and environment.
References [1] Kotteman, M., Kish, A., Iloanusi, C., Bjork, S., & DiRuggiero, J. (2005). Physiological responses of the halophilic archaeon halobacterium sp. strain nrc1 to desiccation and gamma irradiation.Extremophiles, 9(3), 219-227. [2] Sharma K, Gillum N, Boyd JL, Schmid AK. (2012). he RosR transcription factor is required for gene expression dynamics in response to extreme oxidative stress in a hypersaline-adapted archaeon. BMC Genomics, 13,351-367. [3] Dornmayr-Pfafenhuemer M, Legat A, Schwimbersky K, Fendrihan S, Stan-Lotter H. (2011). Responses of Haloarchaea to Simulated Microgravity. Astrobiology, 11(3), 199–205. [4] Potts M. (2001). Dessication tolerance: a simple process?. TRENDS in Microbiology, 9(11), 553-559. [5] Kixmuller D, Greie JG. (2012). An ATP-driven potassium pimp promotes long-term survival of Halobacterium salinarum within salt crystals. Environmental Microbiology Reports, 4(2), 234, 241. [6] Amoozegar MA, Ghasemi A, Razavi MR. (2007). Evaluation of hexavalent chromium reduction by chromateresistantmoderately halophile.
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Alzheimer’s Disease: Current Therapies and Emerging Research Kanan Shah and Vivek Pisharody Introduction he greatest challenge in the development of efective treatments for the neurodegenerative disorder Alzheimer’s Disease (AD) is the lack of scientiic consensus on its cause. Numerous hypotheses and mechanisms have been proposed, but no current hypothesis explains all observed symptoms of this disease. AD is clinically characterized by the rapid loss of cognitive ability and memory. Anatomically, AD begins with the appearance of extracellular deposits of insoluble amyloid-β protein (Aβ), known as senile plaques, and the formation of neuroibrillary tangles (NFTs). Traditionally, two clinically similar forms of AD have been described: Familial Alzheimer’s Diseases (FAD), a hereditary form of AD, and Sporadic Alzheimer’s Disease (SAD), which can develop in individuals with no family history of AD. While numerous hypotheses have been posited, the mechanism by which Alzheimer’s Disease develops is still unknown [1]. It is also uncertain whether the initial causes of FAD and SAD are diferent. Furthermore, although Aβ plaques and NFTs are always found in AD brain, Aβ and NFTs may only represent a inal product of progressive neurodegeneration due to AD, rather than a cause [2]. Despite decades of research, there remains much to be discovered about this mysterious disease. Two Classic Hypotheses Traditionally, two main causal mechanisms for the development and progression of AD have been proposed. First, the Aβ “cascade” hypothesis proposes that excess production of amyloid-β, a small ibrillar peptide, leads to the accumulation of extracellular senile plaques in the spaces around synapses, which in turn lead to neurodegeneration and apoptosis [1]. Aβ is formed by the cleavage of amyloid precursor protein (APP), which is encoded by a gene located on the twenty-irst chromosome. he released amyloid peptide then travels to the extracellular spaces and forms Aβ plaques. Current therapies for AD utilize a variety of mechanisms, but the majority of treatments aim to decrease amyloid production. However, the classical Aβ “cascade” hypothesis has come under scrutiny as Aβ deposits do not correlate with clinical symptoms, and Aβ plaques have been found in the brains of individuals without AD [4]. Unlike insoluble Aβ deposition, soluble Aβ concentration does correlate with cognitive impairment. Recent research indicates that the soluble Aβ oligomers, which are comprised of protoibril Aβ and Aβ-derived difusible ligands (ADDL), are also
toxic [2]. Aβ oligomers are hypothesized to contribute to suppression of long-term potentiation, the strengthening of synapses between cells in memory recall, and may be the major cause of synaptic dysfunction during early stages of AD [1,4]. ADDLs bind to receptors on neurons, thereby changing the structure of synapses and disrupting neural communication [3]. Protoibrils, soluble intermediates found in the process of amyloid ibril formation, may contribute to neuronal death later in the progression of AD [3]. Moreover, evidence has shown that N-APP, a relative of the Aβ protein, may be more signiicant in neural degeneration than the Aβ protein. N-APP, a fragment of APP from its N-terminus, is cleaved from APP by one of the same enzymes that cleaves Aβ. N-APP causes apoptosis by binding to a cell site that induces cell death [3]. he other classical hypothesis centers on the hyperphosphorylation of Tau, a microtubule-associated protein that stabilizes nerve cells’ structures. Tau hyperphosphorylation is thought to cause it to dissociate from microtubules and accumulate in intracellular neuroibrillary tangles (NFTs) [5,6]. When abnormally phosphorylated, Tau reduces its ainity for and dissociates further from microtubules, accumulated in the neuronal perikarya and processed as paired helical ilaments (PHF) [7]. he abnormal NFTs lead to a loss of dendritic microtubules and synapses, membrane degeneration, and ultimately cell death. In addition, hyperphosphorylated Tau also sequesters normal Tau molecules into the aggregates, which in turn have a negative impact on the normal microtubule function. Recent research supports that only the soluble, oligomeric forms of Tau are pathogenic, a result similar to the one found in the Aβ hypothesis[5]. Mutations that increase the risk of early-onset AD and tau hyperphosphorylation are colocalized with genes linked to Aβ plaque production. hese genes encode for APP and for membrane- spanning proteins presenilin-1 (PS-1) and PS-2 that process APP. herefore, it is postulated that either the Tau gene mutation or the accumulation of Aβ plaques can trigger the accumulation of hyperphosphorylated Tau protein [6]. Alternative Pathways and Mechanisms hough the two classical hypotheses described above have dominated AD research for the past quarter century, new research has revealed that a variety of biological mechanisms may be implicated in this disease.
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Street Broad Scientific Glycogen Synthase Kinase 3 Despite its name, the serine/threonine kinase, known as glycogen synthase kinase 3 (GSK 3), has important functions outside of glycogen synthesis, and is known to be crucial to mechanisms as varied as Wnt signaling, apoptosis, cell development and diferentiation, metabolic homeostasis, inlammation, and cell polarity. GSK 3 has been linked to an incredible variety of neurodegenerative diseases [8]. Jope et al. have proposed that inlammation is the causal link between neurodegenerative diseases and GSK 3 as GSK 3 promotes the migration of pro-inlammatory cells and the iniltration of inlammatory molecules into the brain. Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) A rare missense mutation in the gene encoding for TREM2 has been found to interfere with the brain’s ability to prevent the buildup of plaque and is linked to AD. Under normal conditions, the TREM2 gene allows white blood cells in the brain to eliminate the plaque- forming protein Aβ. However, the mutated TREM2 gene reduces white blood cells’ efectiveness in attacking Aβ. People with the mutated gene have ive times as much of a risk of developing AD as they age. In a study of genetic data from around the world, this mutation occurred in 0.5 to 1% of the general population, but in 1 to 2% of patients with AD [11]. his discovery reconsiders the previously ignored inlammation of the brain in AD patients and highlights the role of the immune system in the disease [11]. Translocase of the Outer Mitochondrial Membrane, 40 kD (TOMM40) TOMM40, a recently identiied risk gene for AD on the 19th chromosome, encodes the essential mitochondrial protein import translocase and is adjacent to and in linkage disequilibrium with the apolipoprotein (APOE) gene. Of the three alleles (short, long, and very long), the very long allele is associated with impaired verbal memory recall. his same impairment is seen in APOE ε3/ε4 subjects with a family history of AD [12]. Although APOE ε 4 and TOMM40 are associated with each other, recent research indicates that APOE ε 4 and TOMM40 inluence age-related memory but do so independently of each other. TOMM40 has a signiicant efect only before the age of 60, while APOE ε3/ε4 only has a signiicant efect after the age of 60 [13]. Current herapeutic Approaches As AD has no known cure, current treatment is generally centered on maintaining quality-of-life. Treatments for AD often focus on symptoms; conventional treatments for depression, anxiety, and psychosis are used as they would be in non-AD patients. here are currently two major classes of medication that directly address AD, cholinesterase inhibitors and glutamate inhibitors [14,15].
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REviEw Cholinesterase (ACh) inhibitors Cholinesterase inhibitors, the irst class of AD medication developed, attempt to increase cognitive ability by increasing levels of the neurotransmitter Ach [15]. While the exact mechanism of each drug varies, all drugs in this class work by reducing the efectiveness of acetylcholinesterase (AChE), the enzyme responsible for the breakdown of ACh. Elevated ACh levels temporarily increase the ability of neurons to transfer signals to other neurons, thereby increasing cognition. his class of medication has long been considered a irst line of action against mild to moderate AD. However, cholinesterase inhibitors have severe shortcomings. he efects of cholinesterase inhibitors are short-term, and many patients do not respond to this therapy [16]. Furthermore, these drugs have no efect on the observed anatomy of the disease; Aβ plaques and NFTs remain unchanged. Glutamate Inhibitors he second category of drugs available for AD also attempts to increase neurotransmission, but instead targets glutamate, another neurotransmitter [17]. Keltner and Williams note that sustained glutamate signaling has been linked to cognitive decline and neuronal death through excitotoxicity, a mechanism in which hyperactivity of an enzyme causes cell damage and death. Currently, only one drug in this class, memantine hydrochloride, has been FDA-approved for AD treatment. Memantine HCl has been approved for mild to moderate AD and reduces abnormal, sustained signaling of glutamate while leaving normal glutamate action unafected. However, there is insuicient clinical data to conirm whether memantine hydrochloride will have long-term efects on neurodegeneration. Furthermore, like cholinesterase inhibitors, memantine HCl does not afect NFTs or Aβ plaques [18]. Medicines in Development In addition to these existing classes of medication, Niikura et al. investigated proposed therapeutic options based on the Aβ hypothesis [3]. hese approaches focus on the removal of Aβ plaques. Possible mechanisms of Aβ removal include suppression of the secretases responsible for the production of Aβ from APP, accelerating the rate of natural Aβ degradation by enzymes in the brain, and immunization against Aβ. However, methods to increase the rate of Aβ removal are still in their infancy, and immunization against Aβ in human trials resulted in a signiicant inlammatory response. Niikura et al. reported an alternative method to combat AD-related neurodegeneration. Using Humanin, a novel neuroprotective compound, Niikura demonstrated that neurons can be successfully protected from the damaging efects of Aβ, and hypothesized that suicient neuroprotection, combined with some level of Aβ removal, can avert neuronal death entirely.
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REviEw Computational Models and Advancement in AD Research Recently, developments in computational neuroscience have provided a way to integrate the many factors inluencing the progression of AD, including the relative contribution of cell death, slowing of conduction velocities, and normal aging processes, into a complex system that mediates the interaction between the proposed mechanisms [19]. In 1994, Alvarez and Squire proposed a model of the role of key neural regions involved in AD, the hippocampus and neocortex [20]. hey assumed learning between the two occurs quickly, whereas forgetting occurs at a moderate rate. In addition, intra-neocortical learning and forgetting occur slowly. his model showed the hippocampus slowly teaching the neocortex, and when lesioned, the model simulates the response of AD brain. he advantage of this model is that it shows associations learned in early cycles, but a disadvantage is that it does not perform well on memories learned in later cycles. he model can also be used to determine how much information is lost between neocortical regions and between the neocortex and hippocampus [21]. More recently, Glaw and Skalak have developed a model to test the hypothesis that GSK-3 provides a possible link between Aβ buildup and NFT development [21]. his model found that GSK-3 had a large efect on NFT formation, but very little on plaque formation, with no link found between A plaques and NFTs [22]. Computational models continue to be improved and reused to further analyze data. For example, a 1995 model by Ruppin and Reggia showed how lesions in a neural network lead memory loss, and adding a local compensation factor causes a pattern of functional damage similar to that found in AD. Rowan enhanced this model with techniques more representative of current knowledge of the disease. In Rowan’s model, the high density of local connections leads to synaptic redundancy and increased protection against damage [22]. his model showed that by silencing the output of selected neurons to simulate the efects of axonal binding blocked by NFTs, initial retrieval of remote memories is more reliable than retrieval of recent memories at early stages of damage. If the brain continues to make use of this efect and uses the more readily available remote memories, the recently-stored memories continue to become less reliable and recall performance for recent patterns decreases. his result is similar to that seen in clinical studies [22].
Conclusion he true cause of Alzheimer’s Disease continues to be an enigma, but recent research has elucidated many possible avenues for the development of new therapies. he classical amyloid-β and Tau hypotheses have been insuficient to explain the complexities of this disease; however, newer mechanisms, like GSK3, and genetic targets,
like TOMM40, provide new insights. Furthermore, new computational models may provide the key to developing efective treatments.
References [1] Hooper C, Killick R, Lovestone S. he GSK3 hypothesis of Alzheimer’s disease. Journal of neurochemistry. 2008 Mar;104(6):1433–9. [2] Hernández F, Avila J. he role of glycogen synthase kinase 3 in the early stages of Alzheimers’ disease. FEBS letters. 2008 Nov 26;582(28):3848–54. [3] Niikura T, Tajima H, Kita Y. Neuronal cell death in Alzheimer’s disease and a neuroprotective factor, humanin. Current neuropharmacology. 2006;139–47. [4] Avila J, Medina M. he Role of Glycogen Synthase Kinase-3 ( GSK-3 ) in Alzheimer ’ s Disease. In: De La Monte S, editor. Alzheimer’s Disease Pathogenesis-Core Concepts, Shifting Paradigms and herapeutic Targets. INTECH; 2011. p. 197–210. [5] Maccioni RB, Farías G, Morales I, Navarrete L. he revitalized tau hypothesis on Alzheimer’s disease. Archives of medical research. 2010 Apr;41(3):226–31. [6] Brich J, Shie F-S, Howell BW, et al. Genetic modulation of tau phosphorylation in the mouse. he Journal of neuroscience : the oicial journal of the Society for Neuroscience. 2003 Jan 1;23(1):187–92. [7] Takashima A. GSK-3 is essential in the pathogenesis of Alzheimer’s disease. Journal of Alzheimer’s Disease. 2006;9:309–17. [8] Jope RS, Yuskaitis CJ, Beurel E. Glycogen synthase kinase-3 (GSK3): inlammation, diseases, and therapeutics. Neurochemical research. 2007;32(4-5):577–95. [9] Hur E-M, Zhou F-Q. GSK3 signalling in neural development. Nature reviews Neuroscience. Nature Publishing Group; 2010 Aug;11(8):539–51. [10] Zafra D, Corominola H, Domı J, Gomis R, Guinovart JJ. Sodium Tungstate Decreases the Phosphorylation of Tau hrough GSK3 Inactivation. Journal of Neuroscience Research. 2006;273(October 2005):264–73. [11] Jonsson T, Stefansson H, Ph.D. SS, et al. Variant of TREM2 Associated with the Risk of Alzheimer’s Disease. New England Journal of Medicine. 2012 Nov 14;107–16. [12] De Strooper B. Loss-of-function presenilin mutations in Alzheimer disease. Talking Point on the role of presenilin mutations in Alzheimer disease. EMBO reports. 2007 Feb;8(2):141–6. [13] Caselli RJ, Dueck AC, Huentelman MJ, et al. Longitudinal modeling of cognitive aging and the TOMM40 efect. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. Elsevier Ltd; 2012 Nov;8(6):490–5. [14] Birks J. Cholinesterase inhibitors for Alzheimer’s disease (Review). 2012. [15] hacker PD. Surprising discovery with Alzheimer’s Medication. Drug Discovery Today. 2003;8(9):379–80. Volume 2 | 2012-2013 | 61
Street Broad Scientific [16] Pepeu G, Giovannini MG. Cholinesterase inhibitors and memory. Chemico-biological interactions. Elsevier Ireland Ltd; 2010 Sep 6;187(1-3):403–8. [17] Keltner NL, Williams B. Biological Perspectives Memantine : A New Approach to Alzheimer ’ s Disease. Perspectives in Psychiatric Care. 2003;10(3):4–5. [18] Mark LP, Prost RW, Ulmer JL, et al. Pictorial review of glutamate excitotoxicity: fundamental concepts for neuroimaging. AJNR American journal of neuroradiology. 2001;22(10):1813–24. [19] Jedynak BM, Lang A, Liu B, et al. A computational neurodegenerative disease progression score: Method and results with the Alzheimer’s disease neuroimaging initiative cohort. NeuroImage. Elsevier Inc.; 2012 Nov 15;63(3):1478–86. [20] Alvarez P, Squire LR. Memory consolidation and the medial temporal lobe: a simple network model. Proceedings of the National Academy of Sciences of the United States of America. 1994 Jul 19;91(15):7041–5. [21] Crystal H, Finkel L. Computational approaches to neurological disease. World Scientiic. 1996. [22] Rowan M. Efects of Compensation, Connectivity and Tau in a Computational Model of Alzheimer’s Disease. International Joint Conference on Neural Networks. 2011 Jun 30;1–8.
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Intervertebral Discs and Their Interactions with Different Environments Jin Yoon Introduction Back pain and joint pain from cartilage degeneration are major problems in the world. More than 80 percent of adult population sufers from back pain at some time in their lives [1]. Majority of the pain is due to degeneration of the cartilage that forms intervertebral discs (IVD). IVDâ&#x20AC;&#x2122;s are located between vertebral bodies and serve three major functions: acting as a ligament to hold the vertebrae of the spine together, absorbing shock, and enabling the spine to rotate and bend [2]. hey can wear down from overuse, injury and aging. Because of the painâ&#x20AC;&#x2122;s debilitating efects, there have been many attempts to ix the problem, without much success. One method to ix the problem is to replace the damaged IVD, but IVDâ&#x20AC;&#x2122;s have numerous functions and complex properties that are diicult to imitate. A replacement tissue must be able to distribute the load evenly, resist compression, have viscoelastic property, and provide smooth surface for pivoting. If the replacement tissue cannot fulill even one of these properties, the patient cannot function fully. Many have turned to fusing vertebrae together with prosthesis, while other patients, with smaller lesions, have attempted to regenerate the cartilage. here are a few ways people have employed to restore damaged cartilages. A popular restoration method is by implanting replacement tissue grafts. he patient can either replace the damaged cartilage with small sections from a less weight bearing joint or with a full allograft. his replacement treatment has shown to decrease pain in 70 percent of the patients for two to ive years [2]; however there are few problems associated with this treatment. he replaced cartilage does not last long, so the patient must replace it continuously to avoid pain. Furthermore, allografts often induce immune response, which can be dangerous. A big concern in the U.S. is that these tissue grafts can break down and cause osteolysis [2]. To eliminate the need for donor sites, many have tried to heal or regenerate existing cartilage through natural processes. hese processes focused on either enhancing the environment for regeneration or transplanting chondrocytes to form more tissue. hese techniques have not shown completely successful results, and more so in older populations [1]. he reason for such poor results in regeneration methods is because cartilage is an avascular tissue, which means that nutrients transport and waste removal are much more complicated processes that rely solely on difusion. Another common treatment is by exciting the cartilage
through physical, energy, or pharmacological stimulation. Physical stimulation involves penetrating the subchondral bone through abrasion or drilling. he stimulation creates a full-thickness defect [1] which causes a clot to form and provides a scafold that allows mesenchymal stem cells (MSC) to migrate. Even though this treatment is very common, the results have been mixed, due to random differentiation of MSC into diferent cartilage cells or, sometimes, not even a cartilage cell. Moreover, the new tissue has mechanical properties and durability that are less than that of the original tissue. Both energy stimulation and pharmacological stimulations have also shown ambiguous results, requiring further research. An important aspect of further research that is required to engineer a satisfactory functional scafold or tissue is to understand how a cell knows and interacts with matrix. We currently know that cells react to its environment through physical senses, or phenotypic responses. Knowing the speciic interactions of IVD cells with matrix components could yield more information on the degenerative process and provide novel methods for repair.
Structure of IVDs IVD is a cartilage and a joint that allows lexible motion and absorbs shock. It holds the vertebrae together and limits excessive motion. An IVD is composed of 3 main parts: nucleus pulposus (NP), annulus ibrosus (AF), and vertebral endplate (VEP). NP is a gelatinous structure that contains hydrophilic proteoglycan (PG) and glycosaminoglycan (GAG) chains. hese chains are negatively charged, and they maintain a large amount of water in the IVD. Main role of NP is to support the load and distribute the weight evenly. AF is a concentric, multilayered structure with a regular pattern of collagen type I ibers. AF surrounds NP and supports it by preventing NP from deforming when compressed. VEP is positioned on top and bottom of each IVD. It allows nutrients and waste to travel across it by difusion. Because lower IVDs are so big and the rate of difusion for nutrients is slow, lower IVDs are at much greater risk of degeneration.
Cell-substrate interaction Usually, tissue cells need to adhere to a solid to be viable; hence, they are called anchorage dependent. Yet, we do not know how tissue cells are able to distinguish the stifness of diferent matrices. It is hypothesized that cells anchor and pull on their surroundings to determine the stifness [3]. hese processes partly rely on myosin-based Volume 2 | 2012-2013 | 63
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Figure 1. MRI of IVD showing NP and AF in distinct regions (left). Schematic of spinal column (middle). Anatomy of normal disc with histological stain (right) [2]. contractility and transcellular adhesions to apply forces to substrates. However, tissue cells not only apply forces, but also respond to the resistance by the substrate through cytoskeleton organization [3]. It has been found that cellcell contact promotes the cells to have indistinguishable morphologies, while cells on stif surface difer in their spreading and cytoskeletal organization. his research is done in order to try to understand how cells know to exert greater contractile traction forces on stifer substrates.
Cell-matrix interaction It is crucial for proper interaction to exist between a cell and its extracellular matrix (ECM) because the interaction is a key factor in regulating cell survival, diferentiation, and response to environmental stimuli [4]. Integrin receptors on cell surface link cells to their ECM and are responsible for aforementioned functions. It was found that stained NP cells tested highly positive for laminin, while AF cells had minimal attachment to laminin. his signiies that NP cells readily attach to laminin substrates. In mesenchymal stem cells (MSC), it has been found that the elasticity of the matrix can specify lineage of the cells [7]. Using crosslinking of collagen-I, tissues with a range of matrix elasticity values were created. Elasticity is measured by elastic constant, E, which is the resistance that a cell feels when it deforms the ECM. It was found that MSCs on soft substrates (E of 0.1 -1 kPa) branched and spread, and their branching density approached those of primary neurons under similar conditions. MSCs on stifer substrates (E of 8-17 kPa) became spindle-shaped similar to myoblasts. Finally, on very stif substrates (E of 25-40 kPa), which mimic the crosslinked collagen of osteoids, MSCsâ&#x20AC;&#x2122; morphology was similar to that of osteoblasts. Besides the elasticity of the matrix, it was found that non64 | 2012-2013 | Volume 2
muscle myosin II (NMM II)â&#x20AC;&#x201D;which is predicted to exert force on the substrate through focal adhesions to sense the matrix elasticityâ&#x20AC;&#x201D;plays a role in lineage speciication. he researchers have found that when MSCs are treated with blebbistatin, they are prevented from diferentiating. Blebbistatin is a selective and potent myosin inhibitor that inhibits actin activation of NMM II ATPase activity and blocks migration and cytokinesis in vertebrate cells [7]. Adding blebbistatin during plating MSCs can prevent the cells from branching or spreading; however, if bleb is added 24 hours after plating of the MSCs, no signiicant changes are observed. From this research, it is clear that matrix elasticity needs to be optimized for regeneration.
Cellular responses to load IVD cells are made to withstand pressure. However, it has been found that these cells respond diferently based on the type, duration and magnitude of the load [6]. Diferent loads can cause IVD cells to exhibit either anabolic or catabolic responses. Usually, low to moderate magnitudes of compression or pressure causes an increase in anabolic cell responses; contrarily, high magnitude increases catabolic cell responses. It has been observed that a range of magnitudes or frequency exists for each condition in a cell type that promotes biosynthesis. his means that there is a physiological range of stimuli that can promote maximum biosynthesis and cell-mediated repair. Furthermore, inner AF and NP showed similar responses to diferent loads, while outer AF showed a diferent response; having similar responses suggest that they experience similar stimuli and may respond similarly than cells of the outer AF.
Mechanosensing A study aligning with our goal has been previously
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REviEw done on cardiac cells. Cell-to-cell interactions are very important for the cardiac cells to function properly. Many studies have looked at cell-ECM interactions, but we do not know much about cell-to-cell mechanosensitivity and mechanotransduction. Supporting previous conjectures, results from related studies have shown that substrate stifness does have a signiicant efect on cell shape, myoibrillar maturation, and expression of speciic transcription factors, especially at a certain, optimum stifness. here has been a study done trying to ind the role of intercellular adhesions; they looked into N-cadherin-mediated mechanotransduction on morphology and internal organization of cardiac myocytes [8]. N-cadherin (neuronal calcium-dependent adhesion) is a type-1 transmembrane protein, cell-based subadhesion system that plays a vital role in cell adhesions. Mechanotransduction is any mechanism by which a cell converts mechanical stimulus into chemical activity. hey found that disturbing the assembly of actin with cytochalasin D inhibits cadherinmediated adhesions. Another inding from this experiment was that the cell-spreading area of myocytes was also dependent on the stifness of the substrate. Elasticity, or the stifness of the substrate, was measured using an atomic force microscope. Cells grown on soft substrates did not spread as much as cells grown on stifer substrates as shown in igure 2. It can be deduced from this lab that in addition to ECMmediated forces, cell-to-cell mediated forces have great effect in cell morphology, adhesion, and spread area.
Conclusion here have been many attempts to correct damaged IVDs, but they have all had drawbacks. If we understand how and why IVDâ&#x20AC;&#x2122;s become degenerated, we can come up with a solution. So to understand the process behind regeneration, researchers have made investigations as to how cells behave and react given an outside force, but many more interactions to be researched. Furthermore, we are looking into ways in which cadherins can change
Figure 2. Neonatal ventricular rat myocytes plated on gels of varying stifness (A-F). G (top of page) is a comparative bar graph of cell-spreading area on extracellular matrices of varying stifness [8]. Volume 2 | 2012-2013 | 65
Street Broad Scientific downstream signaling cascades. Cadherins are important because these protein molecules help cells adhere to one another. Without cadherins, cells would not be able to function together and accomplish their goals. At this point, we need to understand how the changes in the substrate can afect cell responses. Elucidating this process can help us ind the mechanical signaling targets and perhaps reverse the degeneration of IVD cells.
References [1] Johnna S Temenof, Antonios G Mikos, Review: tissue engineering for regeneration of articular cartilage, Biomaterials, Volume 21, Issue 5, March 2000, Pages 431-440, ISSN 0142-9612, 10.1016/S0142-9612(99)00213-6. [2] Benjamin R. Whatley, Xuejun Wen, Intervertebral disc (IVD): Structure, degeneration, repair and regeneration, Materials Science and Engineering: C, Volume 32, Issue 2, 1 March 2012, Pages 61-77, ISSN 0928-4931, 10.1016/j.msec.2011.10.011. [3] Discher, Dennis E, Paul Janmey, and Yu-Li Wang. “Tissue cells feel and respond to the stifness of their substrate.” Science 310.5751 (2005) : 1139-1143. [4] Gilchrist, C. L., et al. “Functional Integrin Subunits Regulating Cell-Matrix Interactions in the Intervertebral Disc.” J Orthop Res 25.6 (2007): 829-40. NLM. [5] C.L. Gilchrist, A.T. Francisco, G.E. Plopper, J. Chen, L.A. Setton. Eur Cell Mater. Author manuscript; available in PMC 2012 April 22. Published in inal edited form as: Eur Cell Mater. 2011 June 20; 21: 523–532. [6] Setton LA, Chen J, 2004, Intervertebral disc cell mechanics and biological responses to load. Current Opinion in Orthopedics. 15(5):331-340, October 2004. [7] Adam J. Engler, Shamik Sen, H. Lee Sweeney, Dennis E. Discher. Matrix elasticity directs stem cell lineage speciication. Cell. 2006 August 25; 126(4): 677–689. doi: 10.1016/j.cell.2006.06.044. [8] Chopra A, Tabdanov E, Patel H, Janmey PA, Kresh JY. Cardiac myocyte remodeling mediated by N-cadherindependent mechanosensing. Am J Physiol Heart Circ Physiol. 2011 April. 200(4):H1252-66.
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Effect of Backpack Load on Gait Parameters Alice Li Introduction Most high school students carry huge backpacks that heavily inluence their body posture and gait by adding a lot of additional load onto the student while he or she walks, and the general consensus is that the gait of a backpack-wearing student difers substantially from the gait of an unburdened student [1]. Researchers studied the gait of people of all ages, with or without backpacks, by using force plates and camera systems to record data, although it is possible to use other types of measuring devices [2]. However, few take into account factors such as height and physical itness [3], and virtually none have studied students between the ages of 15 and 18.
Review Overview of Gait Gait Cycle he gait cycle represents the events between successive points of contact of a single foot. here are two sets of terms used to describe the gait cycle, shown in igure 1, but most researchers use the more recent set of terms created by the Rancho Los Amigos Hospital [4]. It consists of the “heel strike” and the “toe-of.” he gait cycle has two basic components: the swing phase and the stance phase. he swing phase (32%-38% of the gait cycle) occurs when the foot is completely of the ground, between a toe-of and
heel strike. It consists of three parts: initial swing, midswing, and terminal swing. he stance phase (62-68% of the gait cycle) occurs when the foot is planted irmly on the ground. It consists of ive parts: initial contact, loading response, mid-stance, terminal stance, and pre-swing. he period when both feet are touching the ground is called the “double limb support time,” and the period when only one foot is on the ground called the “single limb support time.” Other terms used to describe gait are cadence, stride length, and step length. Cadence is the number of steps per minute, stride length is the distance between heel strikes of the same foot, and step length is the distance between the heel strike of one foot and the heel strike of the other [4]. Anatomical Reference System Most clinicians and researchers use a standard anatomical reference system. A person is bisected into right and left halves by the sagittal plane, front and back halves by the coronal plane, and top and bottom halves by the transverse plane. Abduction of a limb segment refers to moving it away from the body, and adduction means the opposite. Flexion refers to bending a joint, whereas extension refers to extending the joint.
Figure 1. he gait cycle and gait terms [4].
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Muscles and Joints he Musculoskeletal System he human body consists of over 200 joints, 206 bones, and about 640 diferent muscles. A possible description for the musculoskeletal system is a machine capable of applying forces to other objects. Muscles create forces; the more muscles used to create the force, the greater the force. All the muscles of the human body working simultaneously in the same direction could move about 22 tons. However, the muscles are arranged so that they work in pairs: one muscle the agonist, the other the antagonist. Working against each other prevents either from overstretching [5]. here are two types of contraction: isotonic and isometric. Isometric contraction occurs when the muscle is activated, but the length of the muscle does not change. Isotonic contraction occurs when the length of the muscle changes, and can be further divided into two types: concentric and eccentric. Concentric contraction, the focus of many studies occurs when the muscle shortens, decreasing the tension upon it. Eccentric contraction occurs when the muscle lengthens, increasing tension [2]. Hip Joint he hip joint consists of the head of the femur and the acetabulum of the pelvis, as shown in igure 3. It can move 140 degrees forward and 15 degrees back, 30 degrees outward and 25 degrees inward, and rotate 90 degrees outward and 70 degrees inward [6]. When both feet are on the ground, no muscle contraction is needed to maintain that posture. However, in a single leg stance, such as during the single limb support phase of walking, the abductor muscles must exert torque upon the hip joint to counteract the torque exerted by the entire body’s weight [5]. Knee Complex he knee complex receives very high loads during dynamic weight bearing, which is why it is one of the most common sports injuries. he knee complex consists of two separate joints: the tibiofemoral joint and the patellofemoral joint. An important part of the knee is the menisci, which act as shock absorbers for the joint and brings about normal movement between the two bones. he knee also uses two ligaments, the anterior cruciate ligament and posterior cruciate ligament, to stabilize the knee during tibia axial rotation [6]. Ankle and Foot Joint he foot consists of 28 diferent bones, and the ankle, 3 bones. he ankle is a hinge joint, and consists of the tibiotalar, ibulotalar, and the tibioibular joints. In addition to the ankle joint, the foot also consists of ive metatarsophalangeal joints, where the toes connect to the rest of the foot. his joint is heavily employed during walking, especially during the toe-of phase [6]. During walking, most of the weight the foot bears is 68 | 2012-2013 | Volume 2
Figure 2. Progression of the center of pressure upon the foot during normal gait [6].
distributed to the rearfoot, or the heel. During the heelstrike, the heel bears almost all of the force exerted by the rest of the leg. Figure 2 shows how the center of pressure upon the sole of the foot changes during the stance phase of a stride. he ankle has a range of motion in the sagittal plane ranging from 10 degrees during dorsilexion, or pointing the toes upward, to 12 degrees during plantarlexion, the inverse to dorsilexion [6]. Normal Gait In 1990 at Helen Hayes Hospital in New York, Kadaba et al. measured spatiotemporal parameters and joint angles of 40 healthy young adults, 28 male and 12 female. He found that the average stride length of his subjects was about 1.35 +/- 0.12 m, an average cadence of 113 +/- 9 steps/min, and a double limb support time of 61% of the gait cycle. About a decade later in Korea, Cho et al.. performed a similar experiment but discovered diferent results which may be attributed to racial diferences between Americans and Koreans, or the fact that Cho et al.’s experiment involved barefoot individuals, while Kadaba did not mention whether or not his subjects wore shoes. In Cho et al.’s data, the average stride length is about 20 centimeters less than Kadaba’s average stride length for both males and females, which can be attributed to height diferences between the two sets of subjects. here is also a 4 steps/min diference between the male cadences. However, their data on stance phase agrees around 61%, and both got similar results regarding female cadences. In Kadaba’s data, the bold line represents the mean values and the dotted lines represent the deviation, while in Cho et al.’s, the dotted lines are the graphs of the females, and the bold lines are the graphs of the males. heir kinematic data seems to match fairly well regarding hip kinematics. As shown in igure 3, their experiments got very similar data in hip lexion and extension as well as hip adduction and abduction, with peak angles of about 40 ° and 6 ° respectively. here seems to be some discrepancy in transverse joint motion, or hip rotation. However, Kadaba’s bold line is an average, while Cho et al. did not average
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Figure 5. Comparison of pelvis kinematics between studies. Cho’s data is on the left, and Kadaba’s on the right.
Figure 3. Comparison of hip kinematics between studies. Cho’s data is on the left, and Kadaba’s on the right .
between the studies. he average pelvic tilt from Kadaba’s study is at about 15°, whereas the average pelvic tilt from Cho et al.’s study is approximately 10°. However, as the deviation is the same for both, and this diference could be attributed to the way the two studies deined their axes of reference. he other graphs look fairly similar, with both maximum pelvic obliquity and maximum pelvic rotation at 5°. Gait Under Load
Figure 4. Comparison of knee kinematics between studies. Cho’s data is on the left, and Kadaba’s on the right.
the data from both genders. If the two were averaged, the graphs from both studies would look more similar. As with the kinematics of the hip, the kinematics of the knee between the two studies are also very similar, with peak knee lexion angles of approximately 60° and peak knee varus angles of around 5°. Like with igure 4, Cho et al.’s graph of knee varus and valgus angles look dissimilar because he did not average the data from males with the data from females. he kinematics of the pelvis also show some diferences
Double Strap Backpack Wearing a backpack increases the double limb support time of the gait and decreases swing time. Wang et al. discovered this trend in 2001 in his experiment with collegeaged students [7]. In the next few years, several other researchers reported the same results with younger students, from age 9 to age 15 [3,8,9]. Connolly et al. discovered the double support time of middle-school students increased from about 19% of the gait cycle when the students walked without a backpack to 21% of the gait cycle when the students walked while wearing a backpack. Similarly, Chow et al. reported that the mean double limb support time of adolescent girls increased from about 11.1% to 12.4% when the load increased from 0% of the wearer’s body weight (BW) to 15% BW as indicated in igure 6. Although these changes in percentage are quite small, they do indicate that there is a small diference between the gait of a person wearing a backpack and the gait of a person not wearing a backpack. here is also discrepancy over the spatiotemporal parameters, especially stride length. Pascoe et al. claimed that wearing any kind of bag decreased the stride length Volume 2 | 2012-2013 | 69
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Figure 6. Efect of load weight on double support time.
Figure 7. Efect of backpack load on peak knee extension moment .
of 11-13 year olds; in contrast, Connolly did not ind any signiicant diference between loaded and unloaded stride lengths. Both Chow et al. and Hong et al. also got results similar to Connolly’s. Hong believes that it is diicult to compare their results to Pascoe et al., as Pascoe did not make any mention of the walking distance or walking velocity of his subjects [10]. Critical Limit Another topic of debate among the pediatric medical community is the “critical limit,” or the weight at which a backpack becomes too heavy and causes dramatic changes in gait, potentially contributing to injury or back pain [8].
Figure 8. Efect of load on trunk inclination [9]. 70 | 2012-2013 | Volume 2
REviEw Chow et al. claimed that the critical limit is between 10-12.5% of the wearer’s body weight using several parameters such as peak knee lexion and peak hip rotation moment. Figure 7 shows the dramatic change in parameter 43, knee extension moment, as soon as the load is increased from 10% BW to 12.5% BW, illustrating the critical value at which the knee begins to react diferently to the load. However, even though the parameters indicate major changes in gait pattern, these changes may not necessarily be detrimental, especially when the rest of the body’s movements are taken into consideration [8]. In a diferent experiment, Hong had his 9 to 10 year old subjects walk four diferent distances in increasing order while wearing the backpacks of diferent weight and recorded their trunk inclination angles for each distance and weight, as shown in igure 8. Note that there is a signiicant increase in trunk angle between 15% BW and 20% BW, suggesting that 15% BW is the critical load, rather than the 10% BW given by Chow’s experiment. he diference between Chow’s experiment and Hong’s experiment can be attributed to gender diferences, as Hong used 9-10 year old boys while Chow used 10-15 year old girls. In addition, Chow came to his conclusion by analyzing gait parameters, while Hong observed the trunk’s angle of inclination. Despite this evidence regarding the critical limit, it does not determine what the critical limit is exactly, or if it even exists. No studies have been done on whether consistently going over the critical limit causes any sort of injury, other than a few surveys of students which give somewhat vague results [1].
Conclusion Many studies have been done on how wearing a backpack afects the person’s gait, all of which have slightly conlicting results. Although many of these diferences can be attributed to demographic diferences in the subject, there is still much research to be done. For instance, few researchers have looked into the diferences between wearing a backpack on only one shoulder instead of on both shoulders. In addition, former studies have not taken into consideration the height and physical condition of their subjects, which may be a factor in a gait’s response to loading [3]. hese factors may be especially signiicant in children and adolescents, as their bodies have not fully
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matured yet. here has also been no research done on 1618 year olds, with most researchers focusing on children around the age of 10-12 or fully grown adults. here is also no consensus on the “critical limit” weight, as diferent researcher’s claims range from 10% BW to 20% BW to no critical limit at all [8,9]. In the United States, the average backpack weight for an elementary school student is 17% BW, with some carrying up to 30% BW or 40% BW. Meanwhile, about 50% of adolescents complain of back pain, reaching more than 60% by adulthood. Back pain is mostly likely related to backpack usage, as wearing a backpack forces the back muscles to lex in response to the torque applied to the body by the backpack, so inding this “critical limit” may be key to preventing musculoskeletal harm [1].
References [1] Cho, S. H., Park, J. M., & Kwon, O. Y. (2004). Gender diferences in three dimensional gait analysis data from 98 healthy Korean adults.Clinical Biomechanics 19(2),145–152 [2] Cottalorda, J., Bourelle, S., & Gautheron, V. (2004). Efects of backpack carrying in children. Orthopedics 27(11), 1172-1175 [3] Winter, D. A. (2004). Biomechanics and motor control of human movement. (3rd ed.). New York, NY: Wiley. [4] Connolly, B. H., Cook, B., Hunter, S., Laughter, M., Mills, A., Nordtvedt, N., & Bush, A. (2008). Efect of backpack carriage on gait parameters in children. Pediatric herapy 20(4), 347-355 [5] Cuccurullo, S. (2004). Physical medicine and rehabilitation board review. New York, NY: Demos Medical Publishing. Retrieved from http://www.ncbi.nlm.nih.gov/ books/NBK27235/ [6] Watkins, J. (1999). Structure and function of the musculoskeletal system. Champaign, IL: Human Kinetics. [7] Nordin, M., & Frankel, V. H. (2001). Basic biomechanics of the musculoskeletal system. (3rd ed.). Philadelphia, PA: Lippincott Williams & Wilkins. [8] Wang, Y., Pascoe, D. D., & Weimar, W. (2001). Evaluation of book backpack load during walking, Ergonomics 44(9), 858-869 [9] Chow, D., Kwok, M., Au-Yang, A., Holmes, A., Cheng, J., Yao, F., & Wong, M.S. (2005). he efect of backpack load on the gait of normal adolescent girls. Ergonomics 48:6, 642-656 [10] Hong, Y. & Cheung, C. (2003). Gait and posture responses to backpack load during level walking in children, Gait and Posture 17(1), 28-33 [11] Pascoe, D. D., Pascoe, D. E., Wang, Y. T., Shim, D. M., & Kim, C. K. (1997). Inluence of carrying book bags on gait cycle and posture of youths, Ergonomics 40(6), 631-641 [12] Kadaba, M. P., Ramakrishnan, H. K., & Wootten, M. E. (1990). Measurement of Lower Extremity Kinematics During Level Walking, Journal of Orthopaedic Research 8(3), 383-392 Volume 2 | 2012-2013 | 71
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Featured Scientist: An Interview with Dr. Robert Lefkowitz
Left to Right: BSS Faculty Sponsor Dr. Jonathan Bennett, Tejas Sundaresan, Dr. Robert Lefkowitz, Emmanuel Assa, Halston Lim. Photo Credit: Brian Faircloth Dr. Robert Lefkowitz is the James B. Duke Professor of Medicine at Duke University. Having studied medicine at Columbia University, he completed his medical residency at Massachusetts General Hospital, after which he shifted his focus into medical research. For the past forty years, he has studied cell signaling mechanisms and receptors, and is most known for his pioneering studies of the molecular pathways and structure of the G-protein coupled receptor. A Howard Hughes Medical Investigator, he received the 2012 Nobel Prize in Chemistry for his work. he BSS staf met with Dr. Lefkowitz for his insight and advice for the aspiring high school scientist after his delivery of the keynote address for the 2013 North Carolina Student Academy of Science meeting hosted on the NCSSM campus. Can you think back to when you were in high school? Back then, what were your favorite activities and academic interests? Let’s see what I looked like back then [Dr. Lefkowitz shows us his yearbook photo from 55 years ago]. his should be 1959. Orchestra, Dynamo. Dynamo was the literary publication. So I was an editor for not a scientiic magazine, but a literary magazine. Swim squad. Biology Club. We had several [sports] teams. Interestingly, a couple of our teams were regularly among the best in the city. Of course, we had a math team, a chess team, etc… Did I have any extracurriculars other than those back then? Not really. Subjects, I loved chemistry really. I took AP Chemistry and that was my major actually… It’s really important to discover what your gifts are. Everyone has gifts, and it’s great to igure out what you’re really good at, because that’s what you want to emphasize. hings that come easy to you. It’s worth thinking about.
You had mentioned earlier in your talk that medical research didn’t really cross your mind until the end of your two years at the NIH, where you had initial “successes” in research. What advice do you have for individuals who are researching but who haven’t obtained similar “successes” yet in their ields? Let’s say I went of to residency, having met with nothing but unrelenting failure for two years. here’s no way – I can’t imagine- I would have gone on into a career in research. But let me tell you something that’s really important; it’s going back to those failure things. his guy Kobilka, by the way [referring to Dr. Brian Kobilka, co-recipient of the 2012 Nobel Prize in Chemistry who was previously a post-doctoral researcher under Dr. Lefkowitz]…If you would have said to me, out of the 250 or so you’ve trained, who were the best: hands down, I would have said Kobilka and this other guy. Now, Kobilka met with no success in my laboratory for two and a half years. If he had left after two years, he’d be practicing cardiology. And this is scary. What I have observed is that someone who is good at [research] will ultimately succeed. If I take a look over the people I’ve trained and made a graph, success versus how long it took them to get 72 | 2012-2013 | Volume 2
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something going, I would say, it’s a reverse correlation. he better people are, the longer it takes. Now it’s not in every case and I’m not saying p is less than .05, but in my mind, there is some correlation. Why might that be so? I have found that the best people are drawn to the most diicult and challenging problems. And the more challenging the problem, the longer it takes to make any headway at all. And two years of failure, in the big picture is nothing.
Many research scientists possess PhDs, while you have a medical doctorate. How has this medical background shaped your research? You’d be amazed how many people who win basic science prizes never got a PhD. … One thing, getting an MD teaches you discipline – that’s for sure. It selects for disciplined people, people who are clinically trained, like Kobilka and myself. In my day, surviving an internship in residency wasn’t a mean feat. You couldn’t even sleep. It really solidiied work ethic, etc… If you are a really good physician, [you are] learning how to interrogate a sick patient. It’s almost like being a prosecuting attorney learning how to cross-examine. Science is the cross examination of a problem: every experiment is a question. You got to ask exactly the right question. And that’s exactly what science is. You have to do the exactly the right experiment. And that’s what’s failure’s about. You ask the wrong question a hundred times. But each time, you learn that’s not the way to go. And then your question gets sharper and sharper. And inally, you ask exactly the right question, and bingo, you got it.
Can you tell us a little about the role of biology in business, based on your experiences. Do you have any advice for students who are interested working in the biotechnology industry? It’s interesting. Name of my company is Trevena…. We started this company ive years ago, and the company, the irst three scientists, the nucleus that formed the company, were three of my senior post-docs, who had been very much involved during the previous ive years in developing the body of science that formed the platform of the company- the idea you could signal down these diferent pathways. My irst piece of advice: become a scientist irst. Really get the background irst. You want to be able to yourself to decide “is this a good opportunity?”. I would say become a scientist, and maybe do a post-doc, and then do business.
Is there any important advice for an aspiring scientist in academia? Figure out what you like. here are three types of ways you can earn a living. One: you have a job, like my secretary, you work 9-5 and you take home a salary as a job. Two: you have a career. It’s not necessarily 9-5 and you do what you need to do to advance in the career. You have a skill you hone and you move up in a hierarchy. And then, the third category is a really small percentage: that’s folks like me. You have a passion. I’ve never felt I work for a living. Why am I doing this? It’s the same reason I was doing this thirty years ago. It’s my sandbox. I play. In a sense I’m always working, but it’s not. So if you can igure it out, is there something you’re just going to love? And, don’t take advice from anybody, you have to igure it out for yourself. In the end, you have to go with your heart. It’ll become obvious to you if you’re attentive. You spend your whole life doing what you do, and its much better if you enjoy doing it.
Twenty years from now, what areas in biology will be really important? here are a couple. Cancer. We got a long way to go there. Cancer is hundreds of diseases. We’re just beginning, in the last decade or so, to really make some headway in understanding some of the basic mechanisms that go awry, mutations that lead to cancer, and how to develop drugs. hat’s got to be a huge area. And neurobiology, how the brain works. We really don’t have, in my way of thinking, good drugs to treat severe mental illness. he drugs that we have are crude instruments. If you hear the side efects, sudden death, bleeding from the nose, vomiting, and cardiac arrest. It’s [drugs] a really blunt instrument. So I think neurobiology and understanding basic neuromechanisms at a molecular level and the tools are in place to do this. So those are my two areas in the next twenty years where fundamental biomedical research can impact upon disease. hese two ields are really ripe for progress.
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