Gas‐Phase Errors Affect DFT‐Based Electrocatalysis Models of Oxygen Reduction to Hydrogen Peroxide

Toward Realistic Models of the Electrocatalytic Oxygen Evolution Reaction

The electrocatalytic oxygen evolution reaction (OER) supplies the protons and electrons needed to transform renewable electricity into chemicals and fuels. However, the OER is kinetically sluggish; it operates at significant rates only when the applied potential far exceeds the reversible voltage. The origin of this overpotential is hidden in a complex mechanism involving multiple electron transfers and chemical bond making/breaking steps. Our desire to improve catalytic performance has then made mechanistic studies of the OER an area of major scientific inquiry, though the complexity of the reaction has made understanding difficult. While historically, mechanistic studies have relied solely on experiment and phenomenological models, over the past twenty years ab initio simulation has been playing an increasingly important role in developing our understanding of the electrocatalytic OER and its reaction mechanisms. In this Review we cover advances in our mechanistic understanding of the OER, organized by increasing complexity in the way through which the OER is modeled. We begin with phenomenological models built using experimental data before reviewing early efforts to incorporate ab initio methods into mechanistic studies. We go on to cover how the assumptions in these early ab initio simulations─no electric field, electrolyte, or explicit kinetics─have been relaxed. Through comparison with experimental literature, we explore the veracity of these different assumptions. We summarize by discussing the most critical open challenges in developing models to understand the mechanisms of the OER.

Advancing H2O2 electrosynthesis: enhancing electrochemical systems, unveiling emerging applications, and seizing opportunities

Hydrogen peroxide (H2O2) is a highly desired chemical with a wide range of applications. Recent advancements in H2O2 synthesis center on the electrochemical reduction of oxygen, an environmentally friendly approach that facilitates on-site production. To successfully implement practical-scale, highly efficient electrosynthesis of H2O2, it is critical to meticulously explore both the design of catalytic materials and the engineering of other components of the electrochemical system, as they hold equal importance in this process. Development of promising electrocatalysts with outstanding selectivity and activity is a prerequisite for efficient H2O2 electrosynthesis, while well-configured electrolyzers determine the practical implementation of large-scale H2O2 production. In this review, we systematically summarize fundamental mechanisms and recent achievements in H2O2 electrosynthesis, including electrocatalyst design, electrode optimization, electrolyte engineering, reactor exploration, potential applications, and integrated systems, with an emphasis on active site identification and microenvironment regulation. This review also proposes new insights into the existing challenges and opportunities within this rapidly evolving field, together with perspectives on future development of H2O2 electrosynthesis and its industrial-scale applications.

Automated versus Chemically Intuitive Deconvolution of Density Functional Theory (DFT)-Based Gas-Phase Errors in Nitrogen Compounds

Catalysis models involving metal surfaces and gases are regularly based on density functional theory (DFT) calculations at the generalized gradient approximation (GGA). Such models may have large errors in view of the poor DFT-GGA description of gas-phase molecules with multiple bonds. Here, we analyze three correction schemes for the PBE-calculated Gibbs energies of formation of 13 nitrogen compounds. The first scheme is sequential and based on chemical intuition, the second one is an automated optimization based on chemical bonds, and the third one is an automated optimization that capitalizes on the errors found by the first scheme. The mean and maximum absolute errors are brought down close to chemical accuracy by the third approach by correcting the inaccuracies in the NNO and ONO backbones and those in N–O and N–N bonds. This work shows that chemical intuition and automated optimization can be combined to swiftly enhance the predictiveness of DFT-GGA calculations of gases.