CAREER: Designing synthesizable, ligand-protected bimetallic nanoparticles and modernizing engineering curriculum through computational nanoscience

点击翻译按钮获取中文摘要

The goal of this project is to develop a novel open-access computational framework for predicting the growth mechanisms and morphologies of ligand-protected metal nanoparticles (NPs). With NPs impacting numerous fields of science and technology, from energy to medicine to the environment, there is a critical need to determine the growth mechanisms of ligand-protected metal NPs and predict NP morphologies that can be synthesized in the laboratory. Although metal nanoparticles (NPs) of different sizes and shapes can be synthesized by colloidal chemistry methods, advances towards controlling NP morphology have been based largely on trial and error experimentation, which is often tedious and costly. The proposed computational framework will employ novel first-principles-based structure-property relationships accounting for structure sensitivity and metal composition. The integration of research and education efforts will focus on modernizing the traditional Chemical Thermodynamics course by introducing animation modules based on cutting-edge nanotechnology examples. Outreach activities are planned through a nanoscale-inspired interactive computer game to engage high school students, including underrepresented minorities, into pursuing STEM careers and increase awareness about the importance of the field of nanotechnology.The proposed research project will combine Density Functional Theory methods with Monte Carlo and Molecular Dynamics simulations, Machine Learning, and scientific computing to develop a novel, open-access computational framework, applicable to the design of ligand-protected NPs. This framework will generate a library of crystal structures and electronic properties of thermodynamically stable, thiolate-protected, Au-based bimetallic NPs, across a range of heterometals and particle morphologies, all under realistic experimental conditions. The proposed work aims to advance current theories on NP stabilization, which are based on simplified, electron counting rules. The proposed computational framework will enable rational design of ligand-protected NPs. It will also elucidate NP growth steps that are experimentally intractable, thus accelerating nanomaterials discovery. The research findings will be made available online for experimental verification.