(537i) Data-Driven Tuning of Silicon Alloy Nanomaterials Properties: Novel Insights from Theory and Experiments | AIChE

(537i) Data-Driven Tuning of Silicon Alloy Nanomaterials Properties: Novel Insights from Theory and Experiments


Choi, Y. - Presenter, Auburn University
Geetha, A. S., Institute for Energy Technology
Martin, C., Auburn University
Wyller, G. M., Institute for Energy Technology
Preston, T. J., Institute for Energy Technology
Silicon-Germanium alloy materials (SiGe) are one of the principal semiconductors used for high‑frequency wireless communication chips, high-speed information communication processors, and modems. With the advent of not only the “Internet of Things” but also 5th generation cellular mobile communications, demand of SiGe semiconductor materials is expected to increase progressively. Silicon alloy materials are also attractive for batteries and energy storage applications. These alloy materials can be synthesized by vapor deposition processes such as Chemical Vapor Deposition (CVD) or Atomic Layer Deposition (ALD) methods to make continuous films, as well as by homogeneous chemical nucleation through pyrolysis to make nanoparticles which can then be tempered to make films. Our study here will focus upon the latter materials synthesis route. However, understanding semiconducting nanomaterials formation from the pyrolysis of mixtures of silane (SiH4) and germane (GeH4) under the mildest conditions is still incomplete. It is because homogeneous gas-phase nanomaterials formation is a complex phenomenon in which hundreds to thousands of species undergo simultaneous reactions. The methodology used in our previous work for thermodynamic properties1 and the kinetics2 of Si nanoparticles was robust enough to validate the experimental results of pyrolysis of monosilane by Wyller et al.3 Wyller et al. was able to detect higher order silanes up to pentasilane isomers from the monosilane pyrolysis by developing an advanced gas chromatography/mass spectrometry (GC/MS) method. Further computational modeling can play a significant role in narrowing the gap between controlled experimental studies and practical operating conditions. Automated network generation techniques is one way to describe the kinetics of inorganic nanoparticle formation and investigate all the possible reactions under certain conditions. Rate coefficients are to be estimated for every elementary step comprising the mechanistic model, and kinetic correlations are used to make this tractable. One common method for predicting activation barriers (Ea) is the Evans-Polanyi correlation; however, these structure-activity correlations require detailed thermochemical information for each reacting species. Unfortunately, there are limited studies available that predict the thermochemical properties of SiGe clusters. For this purpose, we conducted a computational study of hydrogenated silicon-germanium alloy clusters (SixGeyHz,1<X+Y≤6) to predict the thermodynamic and electronic properties.4 The optimized geometries of the SixGeyHz clusters were investigated systematically using quantum chemical calculations and statistical thermodynamics. All electronic energies for the clusters were calculated using Gaussian-n methods, in order to validate our approach, we compared our methodology to other popular composite methods such as the CBS-QB3 method and available observed data. Our studies have established trends in thermodynamic properties (standard enthalpy of formation (ΔHof), standard entropy (So), and heat capacity (Cp)), as a function of cluster composition and structure. Furthermore, we compared HOMO−LUMO energy gaps, and HOMO and LUMO electron distributions in order to gain insight into the electronic stability of the hydrogenated Si, Ge, and SiGe clusters. These properties will be discussed in the context of tailored nanomaterials design and generalized using a machine learning approach.

  1. Adamczyk, A. J.; Broadbelt, L. J., Thermochemical Property Estimation of Hydrogenated Silicon Clusters. J. Phys. Chem. A 2011,115(32), 8969-8982.
  2. Adamczyk, A. J.; Reyniers, M. F.; Marin, G. B.; Broadbelt, L. J., Kinetics of Substituted Silylene Addition and Elimination in Silicon Nanocluster Growth Captured by Group Additivity. ChemPhysChem 2010,11(9), 1978-1994.
  3. Wyller G. M., Preston T. J., Skare M. O., Klette H., Anjitha S. G, Marstein E. S., Exploring thermal pyrolysis of monosilane through gas chromatography-mass spectrometry measurements of higher order silanes. Silicon for the Chemical and Solar Industry XIV, 2018
  4. Choi, Y.; Adamczyk, A.J., Tuning Hydrogenated Silicon, Germanium, and SiGe Nanocluster Properties Using Theoretical Calculations and a Machine Learning Approach. J. Phys. Chem. A 2018, 122, 9851−9868