Evolution of catalysts directed by genetic algorithms in a plug-based microfluidic device tested with oxidation of methane by oxygen

Abstract

This paper uses microfluidics to implement genetic algorithms (GA) to discover new homogeneous catalysts using the oxidation of methane by molecular oxygen as a model system. The parameters of the GA were the catalyst, a cocatalyst capable of using molecular oxygen as the terminal oxidant, and ligands that could tune the catalytic system. The GA required running hundreds of reactions to discover and optimize catalyst systems of high fitness, and microfluidics enabled these numerous reactions to be run in parallel. The small scale and volumes of microfluidics offer significant safety benefits. The microfluidic system included methods to form diverse arrays of plugs containing catalysts, introduce gaseous reagents at high pressure, run reactions in parallel, and detect catalyst activity using an in situ indicator system. Platinum(II) was identified as an active catalyst, and iron(II) and the polyoxometalate H5PMo10V2O40 (POM-V2) were identified as active cocatalysts. The Pt/Fe system was further optimized and characterized using NMR experiments. After optimization, turnover numbers of approximately 50 were achieved with approximately equal production of methanol and formic acid. The Pt/Fe system demonstrated the compatibility of iron with the entire catalytic cycle. This approach of GA-guided evolution has the potential to accelerate discovery in catalysis and other areas where exploration of chemical space is essential, including optimization of materials for hydrogen storage and CO2 capture and modifications.

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