Ambient methane catalysis research is in its infancy, with opportunities to scale to multiple generations of technologies across various use cases. Integrating synthesis and testing with time constants that allow autonomous integration could have a dramatic impact. This advance would enable future efforts for fully autonomous iterative research loops, allowing continuous planning, experimentation, and analysis with autonomous execution and updating of inputs for multi-objective optimization. The process, governed by physical principles or AI, can involve hundreds or thousands of iterations. This study will develop platforms to increase the materials explored by orders of magnitude, enabling combinations of catalyst chemistries, sizes and morphologies with varied support chemistry. This platform will lower the time constant for experimentation, enabling exploration of the most efficient energy sources for driving methane reactions across material systems.

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