Rationalizing Acidic Oxygen Evolution Reaction over IrO2: Essential Role of Hydronium Cation.

The development of active, stable, and more affordable electrocatalysts for acidic oxygen evolution reaction (OER) is of great importance for the practical application of electrolyzers and the advancement of renewable energy conversion technologies. Currently, IrO2 is the only catalyst with high stability and activity, but a high cost. Further optimization of the catalyst is limited by the lack of understanding of catalytic behaviors at the acid-IrO2 interface. Here, in strong interaction with the experiment, we develop an explicit model based on grand-canonical density function theory (GC-DFT) calculations to describe acidic OER over IrO2. Compared to the explicit models reported previously, hydronium cations (H3O+) are introduced at the electrochemical interface in the current model. As a result, a variation in stable IrO2 surface configuration under the OER operating condition from previously proposed complete *O-coverage to a mixture coverage of *OH and *O is revealed, which is well supported by in situ Raman measurements. In addition, the accuracy of predicted overpotential is increased in comparison with the experimentally measured. More importantly, an alteration of the potential limiting step from previously identified *O → *OOH to *OH → *O is observed, which opens new opportunities to advance the IrO2-based catalysts for acidic OER.

Theoretical Prediction and Experimental Verification of IrOx Supported on Titanium Nitride for Acidic Oxygen Evolution Reaction.

Reducing iridium (Ir) catalyst loading for acidic oxygen evolution reaction (OER) is a critical strategy for large-scale hydrogen production via proton exchange membrane (PEM) water electrolysis. However, simultaneously achieving high activity, long-term stability, and reduced material cost remains challenging. To address this challenge, we develop a framework by combining density functional theory (DFT) prediction using model surfaces and proof-of-concept experimental verification using thin films and nanoparticles. DFT results predict that oxidized Ir monolayers over titanium nitride (IrOx/TiN) should display higher OER activity than IrOx while reducing Ir loading. This prediction is verified by depositing Ir monolayers over TiN thin films via physical vapor deposition. The promising thin film results are then extended to commercially viable powder IrOx/TiN catalysts, which demonstrate a lower overpotential and higher mass activity than commercial IrO2 and long-term stability of 250 h to maintain a current density of 10 mA cm-2. The superior OER performance of IrOx/TiN is further confirmed using a proton exchange membrane water electrolyzer (PEMWE), which shows a lower cell voltage than commercial IrO2 to achieve a current density of 1 A cm-2. Both DFT and in situ X-ray absorption spectroscopy reveal that the high OER performance of IrOx/TiN strongly depends on the IrOx-TiN interaction via direct Ir-Ti bonding. This study highlights the importance of close interaction between theoretical prediction based on mechanistic understanding and experimental verification based on thin film model catalysts to facilitate the development of more practical powder IrOx/TiN catalysts with high activity and stability for acidic OER.

Heterostructured Bi-Cu2S nanocrystals for efficient CO2 electroreduction to formate.

The electrochemical CO2 reduction reaction (ECO2RR) driven by renewable electricity holds promise to store intermittent energy in chemical bonds, while producing value-added chemicals and fuels sustainably. Unfortunately, it remains a grand challenge to simultaneously achieve a high faradaic efficiency (FE), a low overpotential, and a high current density of the ECO2RR. Herein, we report the synthesis of heterostructured Bi-Cu2S nanocrystals via a one-pot solution-phase method. The epitaxial growth of Cu2S on Bi leads to abundant interfacial sites and the resultant heterostructured Bi-Cu2S nanocrystals enable highly efficient ECO2RR with a largely reduced overpotential (240 mV lower than that of Bi), a near-unity FE (>98%) for formate production, and a high partial current density (2.4- and 5.2-fold higher JHCOO- than Cu2S and Bi at -1.0 V vs. reversible hydrogen electrode, RHE). Density functional theory (DFT) calculations show that the electron transfer from Bi to Cu2S at the interface leads to the preferential stabilization of the formate-evolution intermediate (*OCHO).

Interpretable Machine Learning of Chemical Bonding at Solid Surfaces.

Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.