Optimizing Organic Electrosynthesis through Controlled Voltage Dosing and Artificial Intelligence

08 May 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Organic electrocatalysis can transform the chemical industry by introducing new, electricity-driven processes that are more energy efficient and that can be easily integrated with renewable energy sources. However, their deployment is severely hindered by the difficulties of controlling selectivity and achieving a large energy conversion efficiency at high current density, due to the low solubility of organic reactants in practical electrolytes. This control can be improved by carefully balancing the mass transport processes and electrocatalytic reaction rates at the electrode diffusion layer through pulsed electrochemical methods. In this study, we explore these methods in the context of the electrosynthesis of adiponitrile, the largest organic electrochemical process in industry. Systematically exploring voltage pulses in the timescale between 5-150 ms, led to a 20% increase in production of ADN and a 250% increase in relative selectivity with respect to the state-of-the-art constant voltage process. Moreover, combining this systematic experimental investigation with artificial intelligence (AI) tools allowed us to rapidly discover drastically improved electrosynthetic conditions, reaching improvements of 30% and 325% in ADN production rates and selectivity, respectively. This powerful AI-enhanced experimental approach represents a new paradigm in electrocatalysis research that can accelerate the deployment of electrochemical manufacturing processes.


Organic electrosynthesis
machine learning
artificial intelligence
artificial neural network
Electrochemical Pulsed Techniques

Supplementary materials

Electronic Supplementary Information V4


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