Programmable Pulsed Acidic Water Oxidation for Enhanced H₂O₂ Production

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.