Computational prediction of hot-electron chemistry: Towards electronic control of catalysis

热电子化学的计算预测：迈向催化的电子控制

• 批准号: MR/X023109/1
• 负责人: Reinhard J. Maurer
• 金额: $ 75.85万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX023109%2F1
• 关键词: Computational; prediction; hot; electron; chemistry

Higher living standards and a growing world population are the drivers behind continuous increases in greenhouse gas emission and industrial energy use. This provides growing pressure on chemical industries to develop more sustainable and efficient chemical transformations based on innovative new technologies. Light-driven catalysis offers a promising route to more sustainable and energy efficient chemical transformations than conventional industrial-scale catalysis by replacing petrochemical reactants and energy sources with abundant feedstocks such as carbon dioxide from the atmosphere and renewable energy from sun light. In addition, the transformation of light energy via excited electrons in metal nanoparticles, so-called "hot" electrons, selectively transfers energy to molecules and enables more specific chemical reactions than conventional catalysis, potentially increasing yield and decreasing unwanted side products. Underlying this unconventional form of chemistry is the intricate coupling of light, hot electrons, and reactant molecules, the lack of understanding of which has inhibited systematic design and study of reaction parameters such as particle size, shape, and optimal light exposure. However, no model currently exists that seamlessly connects industrially relevant design parameters, such as nanoparticle shape, size, light intensity and frequency to reaction rates and turnover frequencies. Such a model can only be constructed by simultaneously accounting for the interplay of light-driven hot-electron formation and hot-electron-driven chemical reaction dynamics. A predictive theory of hot-electron chemistry will support adaptation of this technology in the chemical industry, which holds the potential to significantly reduce the industry's carbon footprint.The aim of this project is to develop and exploit a computational simulation framework to understand, predict, and design light-driven chemical reactions on light-sensitive metallic nanoparticles and surfaces. The underlying vision is to deliver and apply quantum theoretical methods that fill a conceptual and methodological gap by providing an accurate and feasible computational prediction of experimentally measurable chemical reaction rates as a function of catalyst design parameters.During the first funding period, the fellow and his team have developed a highly efficient computational chemistry methodology by combining electronic structure theory, machine learning methodology, and nonadiabatic molecular dynamics methods. These have been applied to scrutinize mechanistic proposals of hydrogen surface chemistry and reactive scattering on metal catalysts in close collaboration with experimental partners. In this second funding period, the focus will be switched to deliver on real-world applications of light-assisted hydrogenation catalysis and carbon dioxide reduction chemistry. The aim is to provide a step-change in mechanistic understanding of light-driven catalysis on the example of carbon monoxide and carbon dioxide transformations to enable rational design of catalyst materials with wide implications for continuous photochemistry and electrochemistry applications in industry. We will construct structure-reactivity relations and reaction rate models relevant to improve the industrial viability of carbon dioxide reprocessing in chemical process engineering.

生活水平的提高和世界人口的增长是温室气体排放和工业能源使用持续增加的驱动因素。这给化学工业带来了越来越大的压力，要求它们基于创新的新技术开发更可持续和更有效的化学转化。与传统的工业规模催化相比，光驱动催化提供了一条更可持续、更节能的化学转化途径，它用丰富的原料（如大气中的二氧化碳和来自阳光的可再生能源）取代石化反应物和能源。此外，光能通过金属纳米粒子中的激发态电子（即所谓的“热”电子）进行转化，选择性地将能量传递给分子，从而实现比传统催化更具体的化学反应，从而潜在地提高产量并减少不需要的副产物。这种非常规化学形式的基础是光、热电子和反应物分子的复杂耦合，缺乏对其的理解抑制了系统设计和研究反应参数，如颗粒大小、形状和最佳光照。然而，目前还没有模型能够无缝地将工业相关的设计参数，如纳米颗粒的形状、大小、光强度和频率与反应速率和周转频率联系起来。这样的模型只能通过同时考虑光驱动的热电子形成和热电子驱动的化学反应动力学的相互作用来构建。热电子化学的预测理论将支持该技术在化学工业中的应用，这有可能显著减少该行业的碳足迹。该项目的目的是开发和利用计算模拟框架来理解、预测和设计光敏金属纳米颗粒和表面上的光驱动化学反应。潜在的愿景是提供和应用量子理论方法，通过提供实验可测量的化学反应速率作为催化剂设计参数的函数的准确和可行的计算预测，填补概念和方法上的空白。在第一个资助期内，该研究员和他的团队通过结合电子结构理论、机器学习方法和非绝热分子动力学方法，开发了一种高效的计算化学方法。与实验伙伴密切合作，这些已应用于审查氢表面化学和金属催化剂反应散射的机制建议。在第二个资助期，重点将转向光辅助氢化催化和二氧化碳还原化学的实际应用。其目的是以一氧化碳和二氧化碳转化为例，为光驱动催化的机理理解提供一个阶段性的变化，从而使催化剂材料的合理设计对工业上的连续光化学和电化学应用具有广泛的意义。我们将建立相关的结构-反应关系和反应速率模型，以提高二氧化碳后处理在化工过程工程中的工业可行性。