Designing Materials Acceleration Platforms for Heterogeneous CO2 Photo(thermal)catalysis

TL;DR

This paper explores how materials acceleration platforms can enhance the discovery and development of heterogeneous CO2 photo(thermal)catalysis for sustainable solar fuels, integrating automation and AI.

Contribution

It proposes a MAP framework tailored for accelerating research in CO2 photo(thermal)catalysis, from materials discovery to scale-up, leveraging automation and AI techniques.

Findings

Review of current experimental automation and AI use

Proposal of a MAP outline for CO2 catalysis research

Identification of key design and performance metrics

Abstract

Materials acceleration platforms (MAPs) combine automation and artificial intelligence to accelerate the discovery of molecules and materials. They have potential to play a role in addressing complex societal problems such as climate change. Solar chemicals and fuels generation via heterogeneous CO2 photo(thermal)catalysis is a relatively unexplored process that holds potential for contributing towards an environmentally and economically sustainable future, and therefore a very promising application for MAP science and engineering. Here, we present a brief overview of how design and innovation in heterogeneous CO2 photo(thermal)catalysis, from materials discovery to engineering and scale-up, could benefit from MAPs. We discuss relevant design and performance descriptors and the level of automation of state-of-the-art experimental techniques, and we review examples of artificial intelligence in data analysis. Based on these precedents, we finally propose a MAP outline for autonomous and accelerated discoveries in the emerging field of solar chemicals and fuels sourced from CO2 photo(thermal)catalysis.