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

该项目的目标是开发一种新的开放获取计算框架，用于预测配体保护金属纳米颗粒（NPs）的生长机制和形态。随着NPs影响着从能源到医学再到环境等众多科学技术领域，迫切需要确定配体保护金属NPs的生长机制，并预测可在实验室合成的NP形态。虽然不同大小和形状的金属纳米颗粒（NPs）可以通过胶体化学方法合成，但控制NPs形态的进展主要基于反复试验，这通常是繁琐和昂贵的。提出的计算框架将采用新的基于第一性原理的结构-性能关系，考虑结构敏感性和金属成分。通过引入基于尖端纳米技术实例的动画模块，研究和教育工作的整合将重点放在传统化学热力学课程的现代化上。计划通过一个以纳米为灵感的互动电脑游戏开展外展活动，吸引高中生，包括代表性不足的少数民族，从事STEM职业，并提高对纳米技术领域重要性的认识。拟议的研究项目将密度泛函数理论方法与蒙特卡罗和分子动力学模拟、机器学习和科学计算相结合，开发一种新的、开放获取的计算框架，适用于配体保护np的设计。该框架将生成一个晶体结构和热力学稳定的电子特性库，硫代酸保护，基于金的双金属NPs，跨越一系列异质金属和颗粒形态，所有这些都在现实的实验条件下。提出的工作旨在推进目前基于简化电子计数规则的NP稳定理论。所提出的计算框架将使配体保护NPs的合理设计成为可能。它还将阐明在实验上难以解决的NP生长步骤，从而加速纳米材料的发现。研究结果将在网上公布，以供实验验证。

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.