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.

更高的生活水平和不断增长的世界人口是温室气体排放和工业能源使用持续增加的驱动因素。这为化学工业提供了越来越多的压力，以基于创新的新技术开发更可持续和有效的化学转化。与传统的工业规模催化相比，光驱动的催化通过用大气中的大气和诸如二氧化碳二氧化碳的大气和可再生能量从太阳光中替换了诸如二氧化碳的含量，从而提供了一种有前途的途径。此外，通过金属纳米颗粒中的激发电子转化，所谓的“热”电子，有选择地将能量转移到分子上，并比常规催化，可能会增加产量并降低不需要的副产物。这种非常规的化学形式的基本是光，热电子和反应物分子的复杂耦合，缺乏对它们的理解抑制了对反应参数的系统设计和研究，例如粒径，形状和最佳光暴露。但是，目前没有任何模型无缝连接与工业相关的设计参数，例如纳米颗粒形状，大小，光强度和频率与反应速率和周转频率。这种模型只能通过同时考虑光驱动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。热电学化学的预测理论将支持化学工业中该技术的适应，这有可能大大减少该行业的碳足迹。该项目的目的是开发和利用一个计算模拟框架，以理解，预测和设计对光敏感金属纳米机构和表面上的光驱动化学反应。潜在的视觉是通过提供和应用量子理论方法来填补概念和方法学上的差距，通过提供对实验可测量的化学反应速率的准确且可行的计算预测，这是催化剂设计参数的函数。在第一个融资期间，该团队和他的团队通过将高效的计算化学方法组合到了电动型方法，而不是电子学方法，而不是机器学习方法，并且是机器学习方法，并且是机器学习方法，以及机器学习方法，并开发出来。这些已应用于与实验伙伴密切合作的金属催化剂上氢表面化学和反应性散射的机理建议。在第二个融资期间，将切换重点以交付光辅助氢化催化和二氧化碳还原化学的现实应用。目的是在一氧化碳和二氧化碳转化的示例中提供对光驱动催化的机械理解，以使催化剂材料的合理设计具有广泛的影响，对连续的光化学和工业中的电化学应用。我们将构建与改善化学过程工程中二氧化碳重新处理的工业生存能力相关的结构反应关系和反应速率模型。