Bimetallic Doped RuO2 with Manganese and Iron as Electrocatalysts for Favorable Oxygen Evolution Reaction Performance

Dual Doping in Precious Metal Oxides: Accelerating Acidic Oxygen Evolution Reaction

Developing a highly active and stable catalyst for acidic oxygen evolution reactions (OERs), the key half-reaction for proton exchange membrane water electrolysis, has been one of the most cutting-edge topics in electrocatalysis. A dual-doping strategy optimizes the catalyst electronic environment, modifies the coordination environment, generates vacancies, and introduces strain effects through the synergistic effect of two elements to achieve high catalytic performance. In this review, we summarize the progress of dual doping in RuO2 or IrO2 for acidic OERs. The three main mechanisms of OERs are dicussed firstly, followed by a detailed examination of the development history of dual-doping catalysts, from experimentally driven dual-doping systems to machine learning (ML) and theoretical screening of dual-doping systems. Lastly, we provide a summary of the remaining challenges and future prospects, offering valuable insights into dual doping for acidic OERs.

Exploring the Impact of Oxygen Vacancies in Co/Pr-CeO2 Catalysts on H2 Production via the Water-Gas Shift Reaction

In this study, we utilized various Pr-doped CeO2 catalysts (Pr=5, 10, 20, and 30 wt.%) as a support medium for the dispersion of cobalt (Co) nanoparticles, aiming to investigate the impact of oxygen vacancies on the water-gas shift (WGS) reaction. Different characterization techniques were employed to understand the insights into the structure-activity relationship governing the performance of Pr doped ceria supported Co catalysts towards WGS reaction. Our findings reveal that Co/Pr-CeO2 catalysts at optimum Pr loading (10 wt.%) exhibit a superior CO conversion (88 %) facilitated by the presence of more oxygen vacancies induced by Pr doping into the CeO2 lattice, as opposed to the performance of the pure Co/CeO2 catalytic system. It was also found that the highest activity was obtained at increased intrinsic oxygen vacancies and strong synergy between Co and Pr/CeO2 support, fostering more favorable CO activation at the interfacial sites, thus accounting for the observed enhanced activity.

Optimal Electrocatalyst Design Strategies for Acidic Oxygen Evolution

Hydrogen, a clean resource with high energy density, is one of the most promising alternatives to fossil. Proton exchange membrane water electrolyzers are beneficial for hydrogen production because of their high current density, facile operation, and high gas purity. However, the large-scale application of electrochemical water splitting to acidic electrolytes is severely limited by the sluggish kinetics of the anodic reaction and the inadequate development of corrosion- and highly oxidation-resistant anode catalysts. Therefore, anode catalysts with excellent performance and long-term durability must be developed for anodic oxygen evolution reactions (OER) in acidic media. This review comprehensively outlines three commonly employed strategies, namely, defect, phase, and structure engineering, to address the challenges within the acidic OER, while also identifying their existing limitations. Accordingly, the correlation between material design strategies and catalytic performance is discussed in terms of their contribution to high activity and long-term stability. In addition, various nanostructures that can effectively enhance the catalyst performance at the mesoscale are summarized from the perspective of engineering technology, thus providing suitable strategies for catalyst design that satisfy industrial requirements. Finally, the challenges and future outlook in the area of acidic OER are presented.

Hollow-structured cobalt sulfide electrocatalyst for alkaline oxygen evolution reaction: Rational tuning of electronic structure using iron and fluorine dual-doping strategy

Utilizing renewable electricity for water electrolysis offers a promising way for generating high-purity hydrogen gases while mitigating the emission of environmental pollutants. To realize the water electrolysis, it is necessary to develop highly active and precious metal-free electrocatalyst for oxygen evolution reaction (OER) which incurs significant overpotential due to its complicated four-electron transfer mechanism. Hence, we propose a facile preparation method for hollow-structured Fe and F dual-doped CoS2 nanosphere (Fe-CoS2-F) as an efficient OER electrocatalyst. The uniform hollow and porous structure of Fe-CoS2-F enlarge the specific surface area and increase the number of exposed active sites. Furthermore, the Fe and F dual-dopants synergistically contributed to the adjustment of electronic structure, thereby promoting the adsorption/desorption of oxygen-containing reaction intermediates on active sites during the alkaline OER procedure. As a result, the prepared Fe-CoS2-F exhibits outstanding OER activity, characterized by a low overpotential of 298 mV to achieve a current density of 10 mA cm-2 and a Tafel slope as small as 46.0 mV dec-1. Based on computational theoretical calculations, the introduction of the dual-dopants into CoS2 structure reduce the excessively strong adsorption energy of reaction intermediate in the rate determining step, leading to effectively promoted electrocatalytic cycle for OER in alkaline environment. This study presents an effective strategy for preparing noble metal-free OER electrocatalysts with promising potential for large-scale industrial water electrolysis.

Probing Changes in the Local Structure of Active Bimetallic Mn/Ru Oxides during Oxygen Evolution

Identifying the active site of catalysts for the oxygen evolution reaction (OER) is critical for the design of electrode materials that will outperform the current, expensive state-of-the-art catalyst, RuO2. Previous work shows that mixed Mn/Ru oxides show comparable performances in the OER, while reducing reliance on this expensive and scarce Pt-group metal. Herein, X-ray photoelectron spectroscopy and X-ray absorption spectroscopy (XAS) are performed on mixed Mn/Ru oxide materials for the OER to understand structural and chemical changes at both metal sites during oxygen evolution. The results show that the Mn-content affects both the oxidation state and local coordination environment of Ru sites. Operando XAS experiments suggest that the presence of MnOx might be essential to achieve high activity likely by facilitating changes in the O-coordination sphere of Ru centers.

Machine Learning Descriptors for Data-Driven Catalysis Study

Traditional trial-and-error experiments and theoretical simulations have difficulty optimizing catalytic processes and developing new, better-performing catalysts. Machine learning (ML) provides a promising approach for accelerating catalysis research due to its powerful learning and predictive abilities. The selection of appropriate input features (descriptors) plays a decisive role in improving the predictive accuracy of ML models and uncovering the key factors that influence catalytic activity and selectivity. This review introduces tactics for the utilization and extraction of catalytic descriptors in ML-assisted experimental and theoretical research. In addition to the effectiveness and advantages of various descriptors, their limitations are also discussed. Highlighted are both 1) newly developed spectral descriptors for catalytic performance prediction and 2) a novel research paradigm combining computational and experimental ML models through suitable intermediate descriptors. Current challenges and future perspectives on the application of descriptors and ML techniques to catalysis are also presented.

Mn-Dopant Differentiating the Ru and Ir Oxidation States in Catalytic Oxides Toward Durable Oxygen Evolution Reaction in Acidic Electrolyte

Designing an efficient and durable electrocatalyst for the sluggish oxygen evolution reaction (OER) at the anode remains the foremost challenge in developing proton exchange membrane (PEM) electrolyzers. Here, a highly active and durable cactus-like nanoparticle with an exposed heterointerface between the IrO₂ and the low oxidation state Ru by introducing a trace amount of Mn dopant is reported. The heterostructure fabrication relies on initial mixing of the Ru and Ir phases before electrochemical oxidation to produce a conjoined Ru/IrO₂ heterointerface. Benefitting from electron transfer at the heterointerface, the low oxidation state Ru species shows excellent initial activity, which is maintained even after 180 h of continuous OER test. In a half-cell test, the Mn-doped RuIr nanocactus (Mn-RuIr NCT) achieves a mass activity of 1.85 A mg_Ir+Ru ^-1 at 1.48 V_RHE , which is 139-fold higher than that of commercial IrO₂ . Moreover, the superior electrocatalytic performance of Mn-RuIr NCT in the PEM electrolysis system ensures its viability in practical uses. The results of the excellent catalytic performance for acidic OER indicate that the heterostructuring robust rutile IrO₂ and the highly active Ru species with a low oxidation state on the catalyst surface drive a synergistic effect.

Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors

Heterogeneous catalysts are rather complex materials that come in many classes (e.g., metals, oxides, carbides) and shapes. At the same time, the interaction of the catalyst surface with even a relatively simple gas-phase environment such as syngas (CO and H₂) may already produce a wide variety of reaction intermediates ranging from atoms to complex molecules. The starting point for creating predictive maps of, e.g., surface coverages or chemical activities of potential catalyst materials is the reliable prediction of adsorption enthalpies of all of these intermediates. For simple systems, direct density functional theory (DFT) calculations are currently the method of choice. However, a wider exploration of complex materials and reaction networks generally requires enthalpy predictions at lower computational cost.The use of machine learning (ML) and related techniques to make accurate and low-cost predictions of quantum-mechanical calculations has gained increasing attention lately. The employed approaches span from physically motivated models over hybrid physics-ΔML approaches to complete black-box methods such as deep neural networks. In recent works we have explored the possibilities for using a compressed sensing method (Sure Independence Screening and Sparsifying Operator, SISSO) to identify sparse (low-dimensional) descriptors for the prediction of adsorption enthalpies at various active-site motifs of metals and oxides. We start from a set of physically motivated primary features such as atomic acid/base properties, coordination numbers, or band moments and let the data and the compressed sensing method find the best algebraic combination of these features. Here we take this work as a starting point to categorize and compare recent ML-based approaches with a particular focus on model sparsity, data efficiency, and the level of physical insight that one can obtain from the model.Looking ahead, while many works to date have focused only on the mere prediction of databases of, e.g., adsorption enthalpies, there is also an emerging interest in our field to start using ML predictions to answer fundamental science questions about the functioning of heterogeneous catalysts or perhaps even to design better catalysts than we know today. This task is significantly simplified in works that make use of scaling-relation-based models (volcano curves), where the model outcome is determined by only one or two adsorption enthalpies and which consequently become the sole target for ML-based high-throughput screening or design. However, the availability of cheap ML energetics also allows going beyond scaling relations. On the basis of our own work in this direction, we will discuss the additional physical insight that can be achieved by integrating ML-based predictions with traditional catalysis modeling techniques from thermal and electrocatalysis, such as the computational hydrogen electrode and microkinetic modeling, as well as the challenges that lie ahead.

A-Site Cation-Ordering Layered Perovskite EuBa0.5Sr0.5Co2-x Fe x O5+δ as Highly Active and Durable Electrocatalysts for Oxygen Evolution Reaction

The developments of high-performance and tolerant catalysts may enable more sustainable energy in the future, especially toward water oxidation. Herein, we report A-site cation-ordering layered perovskite EuBa_0.5Sr_0.5Co_2-x Fe _x O_5+δ (EBSCFx) (x = 0.2-0.6) electrocatalysts. When evaluated for oxygen evolution reaction (OER) in alkaline media, EuBa_0.5Sr_0.5Co_1.6Fe_0.4O_5+δ (EBSCF0.4) exhibits the best catalytic activity among all of these catalysts, as evidenced by the lowest overpotential of 420 mV at a current density of 10 mA cm^-2. Notably, the catalytic activity of EBSCF0.4 is better than that of commercial IrO₂ at the overpotential >460 mV. Furthermore, the EBSCF0.4-20RuO₂ (involving 20 wt % RuO₂) composite catalyst is developed and gives an overpotential as low as 390 mV at 50 mA cm^-2, which is even superior to commercial RuO₂. For overall water splitting, an electrolysis voltage of merely 1.47 V is achieved at 10 mA cm^-2 in an electrolyzer employing EBSCF0.4-20RuO₂ as bifunctional catalysts, with exceptional durability for 24 h. Such a performance outperforms state-of-the-art IrO₂∥Pt/C and RuO₂∥Pt/C couples. According to density functional theory (DFT) calculations, the unique catalytic properties of EBSCF0.4 may benefit from highly active Fe sites with octahedral coordination, and the synergistic effects of Fe and Ru sites in the composite catalyst accelerate the electrochemical water oxidation.