Main Descriptors To Correlate Structures with the Performances of Electrocatalysts

Theoretical insights and design of MXene for aqueous batteries and supercapacitors: status, challenges, and perspectives

Aqueous batteries and supercapacitors are promising electrochemical energy storage systems (EESSs) due to their low cost, environmental friendliness, and high safety. However, aqueous EESS development faces challenges like narrow electrochemical windows, irreversible dendrite growth, corrosion, and low energy density. Recently, two-dimensional (2D) transition metal carbide and nitride (MXene) have attracted more attention due to their excellent physicochemical properties and potential applications in aqueous EESSs. Understanding the atomic-level working mechanism of MXene in energy storage through theoretical calculations is necessary to advance aqueous EESS development. This review comprehensively summarizes the theoretical insights into MXene in aqueous batteries and supercapacitors. First, the basic properties of MXene, including structural composition, experimental and theoretical synthesis, and advantages in EESSs are introduced. Then, the energy storage mechanism of MXene in aqueous batteries and supercapacitors is summarized from a theoretical calculation perspective. Additionally, the theoretical insights into the side reactions and stability issues of MXene in aqueous EESSs are emphasized. Finally, the prospects of designing MXene for aqueous EESSs through computational methods are given.

A Rechargeable Urea-Assisted Zn-Air Battery With High Energy Efficiency and Fast-Charging Enabled by Engineering High-Energy Interfacial Structures

Electrochemical urea oxidation reaction (UOR) offers a promising alternative to the oxygen evolution reaction (OER) in clean energy conversion and storage systems. Nickel-based catalysts are regarded as highly promising electrocatalysts for the UOR. However, their effectiveness is significantly hindered by the unavoidable self-oxidation reaction of nickel species during UOR. To address this challenge, we proposed an interface chemistry modulation strategy to boost UOR kinetics by creating a high-energy interfacial heterostructure. This heterostructure incorporates Ag at the CoOOH@NiOOH heterojunction interface, where strong interactions significantly promote the electron exchanges at the heterojunction interface between -OH and -O groups. Consequently, the improved electron delocalization leads to the formation of stronger bonds between Co sites and urea CO(NH2)2, promoting a preference for urea to occupy Co active sites over OH*. The resulting catalyst, Ag-CoOOH@NiOOH, demonstrates ultrahigh UOR activity with a low potential of 1.33 V at 100 mA cm-2. The fabricated catalyst exhibits a mass activity over 11.9 times greater than the initial cobalt oxyhydroxide. The rechargeable urea-assisted zinc-air batteries (ZABs) achieve a record-breaking energy efficiency of 74.56 % at 1 mA cm-2, remarkable durability (1000 hours at a current density of 50 mA cm-2), and quick charge performances.

Compressively Strained Fe3O4 in Core-Shell Oxygen Reduction Electrocatalyst Boosts Zinc-Air Battery Performance

Fe3O4 is barely taken into account as an electrocatalyst for oxygen reduction reaction (ORR), an important reaction for metal-air batteries and fuel cells, due to its sluggish catalytic kinetics and poor electron conductivity. Herein, how strain engineering can be employed to regulate the local electronic structure of Fe3O4 for high ORR activity is reported. Compressively strained Fe3O4 shells with 2.0% shortened Fe─O bond are gained on the Fe/Fe4N cores as a result of lattice mismatch at the interface. A downshift of the d-band center occurs for compressed Fe3O4, leading to weakened chemisorption energy of oxygenated intermediates, and lower reaction overpotential. The compressed Fe3O4 exhibits greatly enhanced electrocatalytic ORR activity with a kinetic current density of 27 times higher than that of pristine one at 0.80 V (vs reversible hydrogen electrode), as well as potential application in zinc-air batteries. The findings provide a new strategy for tuning electronic structures and improving the catalytic activity of other metal catalysts.

Electron and surface engineering of Ni2P/MnP4 heterojunction as high performance bifunctional electrocatalyst for amperage-level overall water splitting

Producing hydrogen through electrocatalytic overall water splitting with ampere-level current density is still limited by the high cost and poor stability of electrocatalysts. In this work, a new type Ni2P/MnP4 heterojunction composite material was designed and prepared as bifunctional electrocatalyst. Based on XPS spectra and theoretical calculation, the formation of Ni2P/MnP4 heterojunction successfully modulates the local electronic structure of Ni2P and enhances the ionization of H and Ni by increasing the electron transfer rate. Moreover, the special nanovilli structure and superhydropholic/superaerophobic surface of Ni2P/MnP4 heterojunction accelerates the transfer of electrolyte and gaseous products. Benefiting from these advantages, the as-prepared Ni2P/MnP4/CF not only exhibits superior electrocatalytic performance, which can release 10 mA/cm2 current density with a low overpotential of 69 mV and 247 mV for HER and OER respectively, but also shows admirable stability of continuous overall water splitting to drive 1000 mA/cm2 for 180 h without notable activity degradation. We believe this material possesses outstanding potential for industrial applications, and our strategy may provide a new pathway to design relative materials.

Promoting Electrocatalytic Oxygen Reactions Using Advanced Heterostructures for Rechargeable Zinc-Air Battery Applications

In order to facilitate electrochemical oxygen reactions in electrically rechargeable zinc-air batteries (ZABs), there is a need to develop innovative approaches for efficient oxygen electrocatalysts. Due to their reliability, high energy density, material abundance, and ecofriendliness, rechargeable ZABs hold promise as next-generation energy storage and conversion devices. However, the large-scale application of ZABs is currently hindered by the slow kinetics of the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). However, the development of heterostructure-based electrocatalysts has the potential to surpass the limitations imposed by the intrinsic properties of a single material. This Account begins with an explanation of the configurations of ZABs and the fundamentals of the oxygen electrochemistry of the air electrode. Then, we summarize recent progress with respect to the variety of heterostructures that exploit bifunctional electrocatalytic reactions and overview their impact on ZAB performance. The range of heterointerfacial engineering strategies for improving the ORR/OER and ZAB performance includes tailoring the surface chemistry, dimensionality of catalysts, interfacial charge transfer, mass and charge transport, and morphology. We highlight the multicomponent design approaches that take these features into account to create advanced highly active bifunctional catalysts. Finally, we discuss the challenges and future perspectives on this important topic that aim to enhance the bifunctional activity and performance of zinc-air batteries.

One-Pot Etching Pyrolysis to Defect-Rich Carbon Nanosheets to Construct Multiheteroatom-Coordinated Iron Sites for Efficient Oxygen Reduction

Constructing multiheteroatom coordination structure in carbonaceous substrates demonstrates an effective method to accelerate the oxygen reduction reaction (ORR) of supported single-atom catalyst. Herein, the novel etching route assisted by potassium thiocyanate (KCNS) is developed to convert metal-organic framework to 2D defect-rich porous N,S-co-doped carbon nanosheets for anchoring atomically dispersed iron sites as the high-performance ORR catalysts (Fe-SACs). The well-designed KCNS-assisted etching route can generate spatial confinement template to direct the carbon nanosheet formation, etching condition to form defect-rich structure, and additional sulfur atoms to coordinate iron species. Spectral and microscopy analysis reveals that the iron element in Fe-SACs is highly isolated on carbon nanosheet and anchored by nitrogen and sulfur atoms in unsymmetrical Fe-S1N3 structure. The optimized Fe-SACs with large specific surface area could show remarkable alkaline ORR performances with a high half-wave potential of 0.920 V versus RHE and excellent durability. The rechargeable zinc-air battery assembled with Fe-SACs air electrodes delivers a large power density of 350 mW cm-2 and a stable voltage platform during charge and discharge over more than 1300 h. This work proposes a novel strategy for the preparation of single-atom catalysts with multiheteroatom coordination structure and highly exposed active sites for efficient ORR.

Nickel Hydroxide-Based Electrocatalysts for Promising Electrochemical Oxidation Reactions: Beyond Water Oxidation

Transition metal hydroxides have attracted significant research interest for their energy storage and conversion technique applications. In particular, nickel hydroxide (Ni(OH)2), with increasing significance, is extensively used in material science and engineering. The past decades have witnessed the flourishing of Ni(OH)2-based materials as efficient electrocatalysts for water oxidation, which is a critical catalytic reaction for sustainable technologies, such as water electrolysis, fuel cells, CO2 reduction, and metal-air batteries. Coupling the electrochemical oxidation of small molecules to replace water oxidation at the anode is confirmed as an effective and promising strategy for realizing the energy-saving production. The physicochemical properties of Ni(OH)2 related to conventional water oxidation are first presented in this review. Then, recent progress based on Ni(OH)2 materials for these promising electrochemical reactions is symmetrically categorized and reviewed. Significant emphasis is placed on establishing the structure-activity relationship and disclosing the reaction mechanism. Emerging material design strategies for novel electrocatalysts are also highlighted. Finally, the existing challenges and future research directions are presented.

Work Function-Guided Electrocatalyst Design

The development of high-performance electrocatalysts for energy conversion reactions is crucial for advancing global energy sustainability. The design of catalysts based on their electronic properties (e.g., work function) has gained significant attention recently. Although numerous reviews on electrocatalysis have been provided, no such reports on work function-guided electrocatalyst design are available. Herein, a comprehensive summary of the latest advancements in work function-guided electrocatalyst design for diverse electrochemical energy applications is provided. This includes the development of work function-based catalytic activity descriptors, and the design of both monolithic and heterostructural catalysts. The measurement of work function is first discussed and the applications of work function-based catalytic activity descriptors for various reactions are fully analyzed. Subsequently, the work function-regulated material-electrolyte interfacial electron transfer (IET) is employed for monolithic catalyst design, and methods for regulating the work function and optimizing the catalytic performance of catalysts are discussed. In addition, key strategies for tuning the work function-governed material-material IET in heterostructural catalyst design are examined. Finally, perspectives on work function determination, work function-based activity descriptors, and catalyst design are put forward to guide future research. This work paves the way to the work function-guided rational design of efficient electrocatalysts for sustainable energy applications.

Steering the Reaction Pathway of CO2 Electroreduction by Tuning the Coordination Number of Copper Catalysts

Cu-based catalysts are optimal for the electroreduction of CO2 to generate hydrocarbon products. However, controlling product distribution remains a challenging topic. The theoretical investigations have revealed that the coordination number (CN) of Cu considerably influences the adsorption energy of *CO intermediates, thereby affecting the reaction pathway. Cu catalysts with different CNs were fabricated by reducing CuO precursors via cyclic voltammetry (Cyc-Cu), potentiostatic electrolysis (Pot-Cu), and pulsed electrolysis (Pul-Cu), respectively. High-CN Cu catalysts predominantly generate C2+ products, while low-CN Cu favors CH4 production. For instance, over the high-CN Pot-Cu, C2+ is the main product, with the Faradaic efficiency (FE) reaching 82.5% and a partial current density (j) of 514.3 mA cm-2. Conversely, the low-CN Pul(3)-Cu favors the production of CH4, achieving the highest FECH4 value of 56.7% with a jCH4 value of 234.4 mA cm-2. In situ X-ray absorption spectroscopy and Raman spectroscopy studies further confirm the different *CO adsorptions over Cu catalysts with different CN, thereby directing the reaction pathway of the CO2RR.

Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts

As one of the potential catalysts, disordered solid solution alloys can offer a wealth of catalytic sites. However, accurately evaluating their activity localization structure and overall activity from each individual site remains a formidable challenge. Herein, an approach based on density functional theory and machine learning was used to obtain a large number of sites of the Pt-Ru alloy as the model multisite catalyst for the hydrogen evolution reaction. Subsequently, a series of statistical approaches were employed to unveil the relationship between the geometric structure and overall activity. Based on the radial frequency distribution of metal elements and the distribution of ΔGH, we have identified the surface and subsurface sites occupied by Pt and Ru, respectively, as the most active sites. Particularly, the concept of equivalent site ratio predicts that the overall activity is highest when the Ru content is 20-30%. Furthermore, a series of Pt-Ru alloys were synthesized to validate the proposed theory. This provides crucial insights into understanding the origin of catalytic activity in alloys and thus will better guide the rational development of targeted multisite catalysts.

Material Engineering Strategies for Efficient Hydrogen Evolution Reaction Catalysts

Water electrolysis, a key enabler of hydrogen energy production, presents significant potential as a strategy for achieving net-zero emissions. However, the widespread deployment of water electrolysis is currently limited by the high-cost and scarce noble metal electrocatalysts in hydrogen evolution reaction (HER). Given this challenge, design and synthesis of cost-effective and high-performance alternative catalysts have become a research focus, which necessitates insightful understandings of HER fundamentals and material engineering strategies. Distinct from typical reviews that concentrate only on the summary of recent catalyst materials, this review article shifts focus to material engineering strategies for developing efficient HER catalysts. In-depth analysis of key material design approaches for HER catalysts, such as doping, vacancy defect creation, phase engineering, and metal-support engineering, are illustrated along with typical research cases. A special emphasis is placed on designing noble metal-free catalysts with a brief discussion on recent advancements in electrocatalytic water-splitting technology. The article also delves into important descriptors, reliable evaluation parameters and characterization techniques, aiming to link the fundamental mechanisms of HER with its catalytic performance. In conclusion, it explores future trends in HER catalysts by integrating theoretical, experimental and industrial perspectives, while acknowledging the challenges that remain.

Bird's-Eye View of the Activity Distribution on a Catalyst Surface via a Machine Learning-Driven Adequate Sampling Algorithm

Rational design of catalysts relies on a deep understanding of the active centers. The structure and activity distribution of active centers on a surface are two of the central issues in catalysis and important targets of theoretical and experimental investigations. Herein, we report a machine learning-driven adequate sampling (MLAS) framework for obtaining a statistical understanding of the chemical environment near catalyst sites. Combined strategies were implemented to achieve highly efficient sampling, including the decomposition of degrees of freedom, stratified sampling, Gaussian process regression, and well-designed constraint optimization. The MLAS framework was applied to the rate-determining step in NH3 synthesis, namely the N2 activation process. We calculated the produced population function, PA, which provides a comprehensive and intuitive understanding of active centers. The MLAS framework can be broadly applied to other more complicated catalyst materials and reaction networks.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

Single Atom Iridium Decorated Nickel Alloys Supported on Segregated MoO2 for Alkaline Water Electrolysis

Hetero-interface engineering has been widely employed to develop supported multicomponent catalysts for water electrolysis, but it still remains a substantial challenge for supported single atom alloys. Herein a conductive oxide MoO2 supported Ir1 Ni single atom alloys (Ir1 Ni@MoO2 SAAs) bifunctional electrocatalysts through surface segregation coupled with galvanic replacement reaction, where the Ir atoms are atomically anchored onto the surface of Ni nanoclusters via the Ir-Ni coordination accompanied with electron transfer from Ni to Ir is reported. Benefiting from the unique structure, the Ir1 Ni@MoO2 SAAs not only exhibit low overpotential of 48.6 mV at 10 mA cm-2 and Tafel slope of 19 mV dec-1 for hydrogen evolution reaction, but also show highly efficient alkaline water oxidation with overpotential of 280 mV at 10 mA cm-2 . Their overall water electrolysis exhibits a low cell voltage of 1.52 V at 10 mA cm-2 and excellent durability. Experiments and theoretical calculations reveal that the Ir-Ni interface effectively weakens hydrogen binding energy, and decoration of the Ir single atoms boost surface reconstruction of Ni species to enhance the coverage of intermediates (OH*) and switch the potential-determining step. It is suggested that this approach opens up a promising avenue to design efficient and durable precious metal bifunctional electrocatalysts.

DFT-assisted low-dimensional carbon-based electrocatalysts design and mechanism study: a review

Low-dimensional carbon-based (LDC) materials have attracted extensive research attention in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relieve environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). Traditional "trial and error" strategies greatly slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus revealing the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The commonly used calculation methods and the basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge linking the structure and activity and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlated multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. Furthermore, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

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.

Rational Designing of Bimetallic/Trimetallic Hydrogen Evolution Reaction Catalysts Using Supervised Machine Learning

Cost-efficient electrocatalysts to replace precious platinum group metals- (PGMs-) based catalysts for the hydrogen evolution reaction (HER) carry significant potential for sustainable energy solutions. Machine learning (ML) methods have provided new avenues for intelligent screening and predicting efficient heterogeneous catalysts in recent years. We coalesce density functional theory (DFT) and supervised ML methods to discover earth-abundant active heterogeneous NiCoCu-based HER catalysts. An intuitive generalized microstructure model was designed to study the adsorbate's surface coverage and generate input features for the ML process. The study utilizes optimized eXtreme Gradient Boost Regression (XGBR) models to screen NiCoCu alloy-based catalysts for HER. We show that the most active HER catalysts can be screened from an extensive set of catalysts with this approach. Therefore, our approach can provide an efficient way to discover novel heterogeneous catalysts for various electrochemical reactions.