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This review summarizes the recent theoretical and experimental progress in two-dimensional material-based single-atom catalysts for the electrochemical OER. The remaining challenges and an outlook on future directions are highlighted.
Non-precious metal catalysts for acidic OER typically require a high concentration of activity-promoting elements, e.g. , Mn. We describe the high throughput discovery of quinary oxide catalysts with low Mn concentration via mixing with Sb, Sn, and Ti.
Hybrid DFT predicts that Fe-doping of NiOOH changes the OER activity from insensitive to highly sensitive to crystal facet.
Lewis-acidic boron incorporation into amorphous cobalt oxysulfide induces charge redistribution and cooperative OH − adsorption, enabling enhanced OER activity (10 mA cm −2 at 189 mV in 1.0 M KOH) with stable cycling, driven by Fermi-level electronic modulation.
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ACS Spring Meeting 2021, Virtual, April 5-16, 2021
The 67th AVS International Symposium and Exhibition, Virtual, October 24-29, 2021.
A method of manufacturing an electrode by disposing a three-dimensional substrate in a metal nitrate solution, drying, and thermally phosphatizing with a phosphorus source under inert gas to form a metal based phosphate catalyst on the substrate. An electrocatalyst and electrode produced via the method are also provided.
Transition metal carbides and nitrides are interesting non-precious materials that have been shown to replace or reduce the loading of precious metals for catalyzing several important electrochemical reactions. The purpose of this review is to summarize density functional theory (DFT) studies, describe reaction pathways, identify activity and selectivity descriptors, and present a future outlook in designing carbide and nitride catalysts for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), nitrogen reduction reaction (N 2 RR), CO 2 reduction reaction (CO 2 RR) and alcohol oxidation reactions. Furthermore, this topic is of high interest to scientific communities working in the field of electrocatalysis and this review should provide theoretical guidance for the rational design of improved carbide and nitride electrocatalysts.
Accurate and reproducible screening of the electrocatalytic activity of novel materials for Oxygen Evolution Reaction (OER) and Hydrogen Evolution Reaction (HER) requires establishing an easily adoptable harmonized testing protocol. Herein, we describe a robust, instrumentation-independent testing technique utilizing a three-electrode cell with a fully immersed working electrode. Compared to rotating disk electrode (RDE) techniques, this protocol produces current densities close to those obtained in real electrolyzers and eliminates the usage of the expensive RDE apparatus.
We report alkaline water electrolysis at high current densities is plagued by gas bubble generation and trapping in stochastic porous electrodes (e.g., Ni foams), which causes a significant reduction in the number of electrolyte accessible catalyst active sites. Here, 3D printed Ni (3DPNi) electrodes with highly controlled, periodic structures are reported that suppress gas bubble coalescence, jamming, and trapping and, hence, result in rapid bubble release. The 3DPNi electrodes decorated with carbon-doped NiO achieve a high current density of 1000 mA cm -2 in 1.0 m KOH electrolyte at hydrogen evolution reaction and oxygen evolution reaction overpotentials of 245 and 425 mV, respectively. This work demonstrates a new approach to the deterministic design of 3D electrodes to facilitate rapid bubble transport and release to enhance the total electrode catalytic activity at commercially relevant current densities.
Gas bubbles generated by the hydrogen evolution reaction and oxygen evolution reaction during water electrolysis influence the energy conversion efficiency of hydrogen production. Here we survey what is known about the interaction of gas bubbles and electrode surfaces and the influence of gas evolution on practicable devices used for water electrolysis. Here, we survey the physical processes occurring during the life cycle of a bubble, summarize techniques used to characterize gas evolution phenomena in-situ and in practical device environments, and discuss ways that electrodes can be tailored to facilitate gas removal at high current densities. Lastly, we summarize efforts to model the behavior of individual gas bubbles and multiphase flows produced at gas evolving electrodes. We conclude our review with a short summary of outstanding questions which could be answered by future efforts to characterize gas evolution in electrochemical device environments or by improved simulations of multiphase flows.
The enhanced safety, superior energy, and power density of rechargeable metal‐air batteries make them ideal energy storage systems for application in energy grids and electric vehicles. However, the absence of a cost‐effective and stable bifunctional catalyst that can replace expensive platinum (Pt)‐based catalyst to promote oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) at the air cathode hinders their broader adaptation. Here, in this work, it is demonstrated that Tin (Sn) doped β‐gallium oxide (β‐Ga 2 O 3 ) in the bulk form can efficiently catalyze ORR and OER and, hence, be applied as the cathode in Zn‐air batteries. The Sn‐doped β‐Ga 2 O 3 sample with 15% Sn (Sn x =0.15 ‐Ga 2 O 3 ) displayed exceptional catalytic activity for a bulk, non‐noble metal‐based catalyst. When used as a cathode, the excellent electrocatalytic bifunctional activity of Sn x =0.15 ‐Ga 2 O 3 leads to a prototype Zn‐air battery with a high‐power density of 138 mW cm −2 and improved cycling stability compared to devices with benchmark Pt‐based cathode. The combined experimental and theoretical exploration revealed that the Lewis acid sites in β‐Ga 2 O 3 aid in regulating the electron density distribution on the Sn‐doped sites, optimize the adsorption energies of reaction intermediates, and facilitate the formation of critical reaction intermediate (O*), leading to enhanced electrocatalytic activity.
Generating hydrogen from local energy resources such as solar or wind would unlock a low-carbon energy carrier that could be used to reduce greenhouse gas emissions in sectors such as industry and transportation. Yet, the allocation of new or existing power generation solely to hydrogen production remains contentious due to disputes regarding emissions accounting. Photocatalytic (PC) hydrogen production technologies offer a unique solution, as hydrogen is produced directly from solar energy and water, without the need for electricity generation. However, cost projections for all photocatalytic designs to date have suggested that they are not cost competitive compared to conventional electrolysis systems manufactured at scale. Herein, we offer the first illustrative benchmark of cost and carbon intensity of hydrogen produced in a type 2 “Z-scheme” photocatalytic reactor design, which employs suspended semiconducting nanoconductor particles organized into two stacked volumes in a raceway design. The “Z-scheme” system utilizes two separate photoabsorber particles, tuned to drive either the hydrogen evolution reaction or the oxygen evolution reaction individually, connected via a reversible, charge transfer redox couple in solution. Furthermore, the results suggest a highly competitive and scalable technology, that justifies further experimental validation and prototyping in the field.
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.
Abstract A significant issue that bedeviled the commercialization of renewable energy technologies, ranging from low‐temperature water electrolyzers to high‐temperature solid oxide cells, is the lack of high‐performance catalysts. Among various candidates that could tackle such a challenge, perovskite oxides are rising‐star materials because of their unique structural and compositional flexibility. However, single‐phase perovskite oxides are challenging to satisfy all the requirements of electrocatalysts concurrently for practical applications, such as high catalytic activity, excellent stability, good ionic and electronic conductivities, and superior chemical/thermo‐mechanical robustness. Impressively, perovskite oxides with coupled nanocomposites are emerging as a novel form offering multifunctionality due to their intrinsic features, including infinite interfaces with solid interaction, tunable compositions, flexible configurations, and maximum synergistic effects between assorted components. Considering this new configuration has attracted great attention owing to its promising performances in various energy‐related applications, this review timely summarizes the leading‐edge development of perovskite oxide‐based coupled nanocomposites. Their state‐of‐art synthetic strategies are surveyed and highlighted, their unique structural advantages are highlighted and illustrated through the typical oxygen reduction reaction and oxygen evolution reactions in both high and low‐temperature applications. Opinions on the current critical scientific issues and opportunities in this burgeoning research field are all provided.
The optimal choice of d-block metals in single atom catalysts (SACs) is crucial for designing efficient electrocatalysts for activating the Oxygen reduction reaction (ORR)/ Oxygen evolution reaction (OER) in lithiumoxygen batteries (LOBs). Herein, we used the Quantum Mechanics methods to understand the origin of reactivity for a series of 16 d-block metals supported on nitrogen-doped graphene as SACs for ORR and OER in LOBs. Based on the Gibbs free energy calculations, we found that among the 16 SACs investigated, Zn-SAC exhibits the highest electrochemical activity with the lowest overpotential of 0.17 V. Here we then used machine learning (ML) to develop an intrinsic descriptor, phi, that correlates the catalytic activity with electronic and chemical properties of the catalytic centers at the M-N 4 active site on graphene surface. We established a linear relationship between phi and the catalytic activity that provides guidance for designing efficient SACs for electrocatalysis in LOBs. To validate these predictions, we report electrochemical measurements showing that Zn-SAC exhibits an ultra-stable cyclability with reduced overpotentials over Mo-SAC and nitrogen-doped graphene (NG), confirming our theoretical prediction. This fundamental work provides a deep understanding on the rational design of efficient SACs for OER/ ORR in LOBs.