Van Humbeck, JeffreyTalebi, Pooya2023-12-062023-12-062023-12-06Talebi, P. (2023). Data-driven catalyst design for electrochemical CO2 reduction reaction (CO2RR) (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/117634https://doi.org/10.11575/PRISM/42477Carbon dioxide (CO2) poses significant global problems, primarily driving climate change and environmental degradation. Fossil fuel combustion leads to rising temperatures, extreme weather events, and ocean acidification. Addressing this challenge necessitates international cooperation, transitioning to renewable energy sources, and implementing policies to reduce emissions and the CO2content in the atmosphere. Electrochemical CO2 reduction (CO2RR) is a promising strategy to mitigate CO2 emissions and combat climate change. By utilizing renewable energy sources, such as solar or wind, CO2RR employs electrocatalysts to convert carbon dioxide into valuable chemicals and fuels. This technology aims to reduce CO2 levels in the atmosphere and to develop a sustainable and circular carbon economy, offering a potential pathway to tackle the challenges posed by excess carbon dioxide and promoting a greener, more efficient future. Nevertheless, numerous technical challenges must be addressed for successful CO2RR implementation, with a primary concern being the lack of a suitable catalyst for the reaction. Presently, copper stands as the only mono-metallic electrocatalyst capable of catalyzing CO2RR, but its performance remains economically impractical. This thesis focuses on exploring and developing non-copper-based catalysts for CO2RR in an effort to overcome this limitation and advance the feasibility of the process. Chapter 3 introduces a novel approach to identify potential catalysts for CO2RR using high-throughput density functional theory (DFT) calculations. The study screened 800 transition metal nitrides (TMNs) and singled out Co, Cr, and Ti TMNs as the most promising candidates based on thermodynamic analysis, with their stability and activity thoroughly assessed. Additionally, machine learning (ML) regression models were employed to predict binding energies, uncovering that the group number of metals significantly impacts the binding energy of *OH and, consequently, the catalysts' stability. By combining high-throughput DFT screening and ML regression analysis, this study demonstrates an effective means of discovering new energy materials for CO2 reduction. In Chapters 4 and 5, metal sulfide, and oxides are investigated respectively. Using experimental validation, it is shown that the 2D SnS2 and the ABO3 perovskites discussed are promising catalyst materials for CO2RR. Furthermore, in Chapter 4, a novel strategy has been implemented to increase the efficiency of the catalyst. By applying an oscillating potential (as opposed to a static and constant potential) to the cell, the Faradaic efficiency (FE) of the cell is improved significantly.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Cabon dioxide reductionElectrochemistryDFTDensity Functional TheoryMaterials designCatalysisCarbon UtilizationChemistry--PhysicalEnvironmental Sciencesmaster thesis