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• W2969408089 abstract "Significance The electrification of chemical manufacturing can enable the integration of renewable electricity sources into a sustainable chemical industry. The combined experimental and artificial intelligence-enabled approach discussed in this work represents a paradigm shift in the electrosynthesis field and can help accelerate the industry’s transformation. The strategy that we present improves reaction selectivity (by 325%) and production rates (by 30%) for the largest organic electrochemical process in industry, the electrosynthesis of adiponitrile (ADN). These advances are achieved by carefully tuning the electrochemical environment around the electrocatalyst surface and implementing data-driven models to rapidly elucidate optimal reaction conditions unpredictable by existing physical models. Although this approach was demonstrated for ADN production, it can serve as a universal model for sustainable electrosynthesis development." @default.
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• W2969408089 date "2019-08-21" @default.
• W2969408089 modified "2023-10-15" @default.
• W2969408089 title "Optimizing organic electrosynthesis through controlled voltage dosing and artificial intelligence" @default.
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• W2969408089 doi "https://doi.org/10.1073/pnas.1909985116" @default.
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