电合成
生化工程
过程(计算)
计算机科学
纳米技术
工艺工程
电化学
化学
材料科学
工程类
电极
物理化学
操作系统
作者
Daniela E. Blanco,Bryan Lee,Miguel A. Modestino
标识
DOI:10.1073/pnas.1909985116
摘要
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.
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