Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
DOI: https://doi.org/10.29363/nanoge.matsus.2024.219
Publication date: 18th December 2023
Photocatalytic systems for the reduction of CO2 into fuels and platform chemicals are multi-variable multi-metric systems. As such, long-established optimisation approaches are poorly suited maximising system performance because they aim to optimise one performance metric while sacrificing the others and thereby limit overall system performance. I will present work to address this multi-metric challenge by defining a metric for holistic system performance that takes all figures of merit into account, and employing a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic system optimisation accessible for human experimentalists. The test platform is a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO reduction, which we experimentally optimised to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the dataset with machine learning algorithms allows quantification of each parameter’s effect on overall system performance, revealing that the buffer concentration is the dominating parameter for optimal performance with nearly four times more importance than the catalyst concentration. The expanded use and standardisation of this methodology to define and optimise holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.
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