Programmable Pulsed Acidic Water Oxidation for Enhanced H <sub>2</sub> O <sub>2</sub> Production
Yexing Tian, Huixin Xiang, Kong Meng, Yong Yan, Ge Chen, Chuanhao Yao
Abstract
Abstract Hydrogen peroxide (H 2 O 2 ) is a high‐value, eco‐friendly chemical with significant medical and industrial applications. H 2 O 2 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 H 2 O 2 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 H 2 O 2 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 H 2 O 2 yields in comparison to constant potential electrolysis (CPE). Furthermore, it was revealed that H 2 O 2 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 H 2 O 2 . The findings also confirm that H 2 O 2 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.