Programmable Pulsed Acidic Water Oxidation for Enhanced H2O2 Production

Abstract

Hydrogen peroxide (H2O2) is a high-value, eco-friendly chemical with significant medical and industrial applications. H2O2 electrosynthesis via water oxidation reaction (WOR) in acidic environments is crucial yet challenging. In this study, we introduce a novel approach by employing programmable pulse potential electrolysis (PPE) with machine learning (ML) optimization to synthesize H2O2 in acidic conditions using a Boron-doped diamond (BDD) catalyst. This approach achieved a high Faradaic efficiency (FE) of up to 64.16% and a H2O2 production rate of 25.62 µmol cm−2 min−1, which represents a 28.9-fold increase in FE and a 51.8-fold boost in H2O2 yields in comparison to constant potential electrolysis (CPE). Furthermore, it was revealed that H2O2 generation influenced by the nonFaradaic current stage at the anode. Meanwhile, hydrogenation processes at the cathode increase the abundance of C─H functional groups on the surface of BDD catalyst, which in turn enhances the production of H2O2. The findings also confirm that H2O2 is produced through a step involving •OH radical formation, as supported by both experimental observations and DFT simulations. This study not only demonstrates the significant potential of PPE in WOR but also underscores the powerful role of ML in optimizing complex multi-parameter experimental conditions.