Computational prediction of hot-electron chemistry: Towards electronic control of catalysis
Lead Research Organisation:
University of Warwick
Department Name: Chemistry
Abstract
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.
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.
