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

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力。这给化学工业带来了越来越大的压力，要求它们在创新技术的基础上开发更可持续和更有效的化学转化。光驱动的等离子体催化提供了一种比传统工业规模催化更可持续和节能的化学转化的有前途的途径，通过用丰富的原料（如来自大气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，光能可以通过金属纳米颗粒中的激发电子（所谓的“热”电子）选择性地转移到分子中，并实现比传统催化更特定的化学反应，从而可能提高产率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，对这一点缺乏了解阻碍了对反应参数（如颗粒大小、形状和最佳曝光）的系统设计和研究。热电子化学的预测理论将支持这项技术在化学工业中的应用，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，预测和设计光敏金属纳米颗粒和表面上的光驱动化学反应，即所谓的等离子体纳米催化剂。该奖学金背后的愿景是提供量子理论方法，通过提供准确和可行的计算预测实验可测量的化学反应速率作为与该技术的实际应用相关的催化剂设计参数的函数，填补概念和方法上的空白。在与实验项目合作伙伴的协同作用下，该研究员将领导一个由2名博士后研究人员组成的研究团队，开发高效的计算化学方法，该方法将用于审查机制建议，支持和指导光驱动等离子体二氧化碳还原化学的实验工作，并构建与提高该技术的工业可行性相关的反应速率模型。其目的是提供一个阶跃变化的光驱动等离子体还原催化的一氧化碳和二氧化碳转化的例子，使催化剂材料的合理设计具有广泛的影响，连续光化学和电化学应用在工业中的机械理解。这些应用将通过研究员与领先的化学和石化公司的持续参与来探索。通过这个项目，研究员将建立一个国际跟踪记录，通过促进现有的和建立新的合作，目标是成为一个公认的研究人员在这个年轻的领域。

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 plasmonic 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 sunlight. In addition, light energy can selectively be transferred via excited electrons in metal nanoparticles, so-called "hot" electrons, 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. A predictive theory of hot-electron chemistry will support the 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, so-called plasmonic nanocatalysts. The vision behind this fellowship is to provide quantum theoretical methods that fill a conceptual and methodological gap by providing accurate and feasible computational prediction of experimentally measurable chemical reaction rates as a function of catalyst design parameters relevant to the real-world application of this technology. In synergy with experimental project partners, the fellow will lead a research team of 2 postdoctoral researchers to develop highly efficient computational chemistry methodology, which will be applied to scrutinize mechanistic proposals, support and guide experimental efforts on light-driven plasmonic carbon dioxide reduction chemistry, and to construct reaction rate models relevant to improve the industrial viability of this technology. The aim is to provide a step-change in the mechanistic understanding of light-driven plasmonic reduction catalysis on the example of carbon monoxide and carbon dioxide transformation to enable rational design of catalyst materials with wide implications for continuous photochemistry and electrochemistry applications in industry. These applications will be explored by continuous engagement efforts of the fellow with leading chemical and petrochemical companies. With this project, the fellow will establish an international track record by fostering existing and establishing new collaborations with the goal to become a recognized researcher in this comparably young field.