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Abstract
This dissertation presents a comprehensive exploration of the electrochemical reduction of carbon dioxide (CO2) at hybrid organic-inorganic interfaces, specifically focusing on the role of self-assembled monolayers (SAMs) on copper (Cu) surfaces to enhance the conversion of CO2 into valuable C2 products through a series of multi-scale simulations. Chapters 1 and 2 lay the foundation by introducing the project's scope, computational methodologies, and the theoretical underpinnings of CO2 electroreduction on metal surfaces.
Chapters 3 and 4 delve into the heart of the investigation, revealing how SAM-modulated Cu catalysts significantly improve CO2 reduction's selectivity and activity toward C2 chemicals. The research uncovers that the introduction of SAMs leads to a decrease in surface energy, which suppresses surface reconstruction, and facilitates CO2 activation and C-C coupling through the creation of dual active sites and the induction of confinement effects and localized electric fields. Moreover, the impact of SAMs' physical properties, such as coverage, conformation, and carbon chain length, on the catalytic performance are thoroughly examined, emphasizing the critical role of surface modifications in optimizing catalysis.
Chapter 5 extends the dissertation's scope by incorporating machine learning techniques to enhance the efficiency of computational simulations for field-enhanced catalysis. Specifically, it introduces a graph neural network-based algorithm that significantly accelerates DFT calculations on predicting field-dependent energetics with acceptable accuracy, allowing for rapid exploration of catalyst systems in the presence of large fields, for the application of electrostatic catalysis, electrocatalysis, fuel cells, and plasma catalysis.
In conclusion, this dissertation not only advances our understanding of CO2 electroreduction at the hybrid organic-inorganic interfaces but also highlights the transformative potential of integrating machine learning techniques into catalysis research. The findings contribute significantly to the development of more efficient and selective catalysts for sustainable chemical synthesis, offering insights that could guide future experimental efforts and catalytic designs.
