Machine-learning-driven integrated probing of oxygen-vacancy distribution and ionomer morphology in iridium oxide catalyst–ionomer nanocomposite electrode for water electrolyzer

Abstract

Tuning the oxygen vacancy (V_o) content and spatial distribution of the ionomer in IrO_2-x–ionomer nanocomposite electrodes is a crucial strategy for developing stable and efficient water electrolyzers. This underscores the necessity of developing a methodology capable of jointly mapping the V_o and ionomer distributions with high spatial resolution. However, simultaneously visualizing and quantifying these two critical features in heterogeneous nanocomposite systems remains challenging. Exploiting the advantages of scanning probe-based electron energy-loss spectroscopy spectrum imaging (EELS SI) for identifying defect states and chemical phases on the nanoscale, we propose an efficient machine-learning-driven electron spectroscopic method for mapping the V_o and ionomer distributions over IrO_2-x–ionomer nanocomposites at high resolution. Integrating spectroscopic imaging and machine learning offers a novel solution for disentangling overlapping spectral features in complex nanocomposites. Based on the high-throughput data processing of large EELS SI datasets, our approach allows statistical assessment of the degrees of V_o homogeneity and ionomer coverage in the IrO_2-x–ionomer composite electrode. We found that the local V_o concentrations are closely related to the degree of local ionomer coverage over the IrO_2-x catalyst particles. This suggests that the surface charge density altered by V_o directly affects the electrostatic interactions governing ionomer adsorption. Because this machine-learning-driven EELS SI method is optimized for grouping and classifying C K- and O K-edges from unsupervised classes, it can be widely used as an efficient tool for characterizing the V_o distribution and differentiating carbon-based chemical phases in various vacancy-tailored oxide catalyst–ion-conducting polymer electrodes.