Machine Learning Assisted Analysis of Electrochemical H2O2 Production

Abstract

The two-electron water oxidation reaction (2e– WOR) provides an option for on-site production of the valuable chemical hydrogen peroxide (H2O2). A major challenge for the 2e– WOR is the need to improve its selectivity toward H2O2 over O2 production through the oxygen evolution reaction. Electrolyte engineering has shown great promise in improving the selectivity and production rates toward H2O2. However, experimental efforts in optimizing the electrolyte only yield results at discrete conditions, which does not provide a complete picture of performance over the entire parameter range. Here, we apply a machine learning assisted prediction to map out the performance of electrochemical H2O2 production over the continuous parameter space of electrolyte composition and applied potential. We collected experimental data of faradaic efficiencies (FEH2O2) and current densities toward H2O2 (JH2O2) as functions of the applied potential and carbonate ion mole fractions (HCO3– and CO32–) in the electrolyte. The data were then used to train a support vector regression model with 5-fold cross-validation. The accuracy of the model was verified against additional experimental results. The model identified that a maximum H2O2 current density of 2.16 mA/cm2 can be achieved from an optimized bicarbonate ion mole fraction of 0.225 at an applied potential of 3.25 V vs RHE. Lastly, the continuous model enables us to evaluate the thermal efficiency, H2O2 electricity cost, and time needed to produce a set amount of H2O2 over a broad range of electrolytes and applied potential conditions. This work demonstrates how machine learning enables the use of discrete experimental points to construct a continuous picture of electrochemical H2O2 production with high accuracy.