Computational Study of Au-Si Nanoalloy Clusters: A DFT Based Approach Prabhat Ranjan1, Ajay Kumar1, Tanmoy Chakraborty*2 1 - Dept. of Mechatronics, 2-Dept. of Chemistry Manipal University Jaipur *Email: tanmoy.chakraborty@jaipur.manipal.edu
Introduction
Result Table: Collection of computed DFT based descriptors of AumSi (m=1-8) in eV
Gold nanoclusters are very much popular due to its unique electronic, optical and magnetic properties. These nanoclusters have applications in radiotherapy, photothermal therapy and imaging of cancer cells. It is well known fact from a large number of previous studies that impurity atoms strongly influence the geometric, structural, magnetic and electronic properties of doped gold clusters that are sensitive towards the dopant atom nature. Silicon doped metal nanoclusters are topics of great interest due to its extensive applications in the field of microelectronics, catalysis, biomedicine and jewelry industry.
Species HUMO-LUMO Gap Electronegativity
AuSi Au2Si Au3Si Au4Si Au5Si Au6Si Au7Si Au8Si
1.632 1.306 1.687 3.337 1.877 1.387 1.931 1.823
4.680 5.224 4.952 4.897 4.367 4.938 5.156 5.211
Hardness
Softness
Electrophilicity Index
0.816 0.653 0.843 1.687 0.938 0.693 0.965 0.911
0.612 0.765 0.592 0.296 0.532 0.720 0.517 0.548
2.340 2.612 2.476 2.448 2.183 2.469 2.578 2.605
4 3.5 3
R² = 0.905
2.5
HOMO-LUMO Gap
2 1.5 1 0.5 0 0
0.2
0.4
0.6
0.8
1
Global Softness
Figure 1. Nanoalloy clusters of Au2Si, Au4Si and Au6Si
Figure 2: A linear correlation between Global Softness Vs HOMO-LUMO Gap
Discussion
Methodology
A nice correlation is established between computed HOMO-LUMO energy gap with our computed descriptors.
Density Functional Theory (DFT) is one of the most successful approach of quantum mechanics to study the electronic properties of materials in terms of quantitative descriptors. Due to its computational friendly nature, DFT is very much popular to study the many-body systems. Conceptual DFT gives a new insight into the study of chemical reactivity.
Our evaluated hardness runs hand in hand with the energy gap of Si doped Au Nano clusters. From the table, it is clear that Au4Si is least reactive species whereas AuSi will exhibit maximum response. The high value of regression coefficient between HOMO-LUMO gap and Global Softness validates our predicted model.
Optimization of the structures of instant nanoalloy clusters have been performed using Gaussian 03, with exchange correlation GGA and basis set LANL2DZ. From the optimized geometries some important conceptual DFT based descriptors viz. Global Hardness (η), Global Softness (S), Global Electrophilicity Index (ω) and Global Electronegativity (χ) have been calculated.
References 1. Priyanka, K. Dharamvir, J. Phys. Chem. Chem.Phys. 15 (2013) 12340-47. 2. S. Gautam, N. Goel K. Dharmvir, RSC Adv., 4 (2014) 13927-33. 3. P. Ranjan, S. Venigalla, A. Kumar, T. Chakraborty, new Front. Chem. 23 (2014) 111-122. 4. P. Ranjan et. al, Mat. Sci.-Pol. 33(2015) 719-724.
Acknowledgement All of the authors are thankful to the management of Manipal University Jaipur for providing the research and financial support.