Optimization of the facet structure of transition-metal catalysts applied to the oxygen reduction reaction

Nano-architectonics of Pt single-atoms and differently-sized nanoparticles supported by manganese-oxide nanosheets and impact on catalytic and anti-biofilm activities

Hybrid-nanozymes are promising in various applications, but comprehensive comparison of hybrid-nanozymes composed of single-atoms or nanoparticles on the same support has never been made. Here, manganese-oxide nanosheets were loaded with Pt-single-atoms or differently-sized nanoparticles and their oxidase- and-peroxidase activities compared. High-resolution Transmission-Electron-Microscopy and corresponding Fast Fourier Transform imaging showed that Pt-nanoparticles (1.5 nm diameter) had no clear (111) crystal-planes, while larger nanoparticles had clear (111) crystal-planes. X-ray Photo-electron Spectroscopy demonstrated that unloaded nanosheets were composed of MnO2 with a high number of oxygen vacancies (Vo/Mn 0.4). Loading with 7.0 nm Pt-nanoparticles induced a change to Mn2O3, while loading with 1.5 nm nanoparticles increased the number of vacancies (Vo/Mn 1.2). Nanosheets loaded with 3.0 nm Pt-nanoparticles possessed similarly high catalytic activities as Pt-single-atoms. However, loading with 1.5 nm or 7.0 nm Pt-nanoparticles yielded lower catalytic activities. A model is proposed explaining the low catalytic activity of under- and over-sized Pt-nanoparticles as compared with intermediately-sized (3.0 nm) Pt-nanoparticles and single-atoms. Herewith, catalytic activities of hybrid-nanozymes composed of single-atoms and intermediately-sized nanoparticles are put a par, as confirmed here with respect to bacterial biofilm eradication. This conclusion facilitates a balanced choice between using Pt-single-atoms or nanoparticles in further development and application of hybrid-nanozymes.

Crystalline-Amorphous Heterophase PdMoCrW Tetrametallene: Highly Efficient Oxygen Reduction Electrocatalysts for a Long-Term Zn-Air Battery

Metallenes have received sustained attention owing to their unique microstructure characteristics and compelling catalytic applications, but the synthesis of multielement crystalline-amorphous metallenes remains a formidable challenge. Herein, we report a one-step wet chemical reduction method to synthesize composition-tunable crystalline-amorphous heterophase PdMoCrW tetrametallene. As-synthesized PdMoCrW tetrametallene is composed of approximately six to seven atomic layers and has flexible crimpiness, a crystalline-amorphous heterophase structure, and high-valence metal species. Time-dependent experiments show that PdMoCrW tetrametallene follows a three-step growth mechanism that includes nucleation, lateral growth, and atom diffusion, respectively. The novel ultrathin structure, optimized Pd electronic structure, and hydrophilic surface together greatly promote the activity and stability of PdMoCrW tetrametallene in the alkaline oxygen reduction reaction. Pd75.9Mo9.4Cr8.9W5.8/C exhibits excellent mass and specific activities of 2.81 A mgPd-1 and 4.05 mA cm-2, which are 20.07/14.46 and 23.42/16.20 times higher than those of commercial Pt/C and Pd/C, respectively. Furthermore, a Zn-air battery assembled using Pd75.9Mo9.4Cr8.9W5.8/C as a cathode catalyst achieves a peak power density of 156 mW cm-2 and an ultralong durability of 329 h. This study reports an effective strategy for constructing crystalline-amorphous quaternary metallenes to advance non-Pt electrocatalysts toward oxygen reduction reaction (ORR) performance and for a Zn-air battery.

Interfacial Electron Transfer in Chemical and Biological Transformation of Pollutants in Environmental Catalysis

Interfacial electron transfer (IET) is essential for chemical and biological transformation of pollutants, operative across diverse lengths and time scales. This Perspective presents an array of multiscale molecular simulation methodologies, supplemented by in situ monitoring and imaging techniques, serving as robust tools to decode IET enhancement mechanisms such as interface molecular modification, catalyst coordination mode, and atomic composition regulation. In addition, three IET-based pollutant transformation systems, an electrocatalytic oxidation system, a bioelectrochemical spatial coupling system, and an enzyme-inspired electrocatalytic system, were developed, demonstrating a high effect in transforming and degrading pollutants. To improve the effectiveness and scalability of IET-based strategies, the refinement of these systems is necessitated through rigorous research and theoretical exploration, particularly in the context of practical wastewater treatment scenarios. Future endeavors aim to elucidate the synergy between biological and chemical modules, edit the environmental functional microorganisms, and harness machine learning for designing advanced environmental catalysts to boost efficiency. This Perspective highlights the powerful potential of IET-focused environmental remediation strategies, emphasizing the critical role of interdisciplinary research in addressing the urgent global challenge of water pollution.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Shape-Controlled Synthesis of Platinum-Based Nanocrystals and Their Electrocatalytic Applications in Fuel Cells

To achieve environmentally benign energy conversion with the carbon neutrality target via electrochemical reactions, the innovation of electrocatalysts plays a vital role in the enablement of renewable resources. Nowadays, Pt-based nanocrystals (NCs) have been identified as one class of the most promising candidates to efficiently catalyze both the half-reactions in hydrogen- and hydrocarbon-based fuel cells. Here, we thoroughly discuss the key achievement in developing shape-controlled Pt and Pt-based NCs, and their electrochemical applications in fuel cells. We begin with a mechanistic discussion on how the morphology can be precisely controlled in a colloidal system, followed by highlighting the advanced development of shape-controlled Pt, Pt-alloy, Pt-based core@shell NCs, Pt-based nanocages, and Pt-based intermetallic compounds. We then select some case studies on models of typical reactions (oxygen reduction reaction at the cathode and small molecular oxidation reaction at the anode) that are enhanced by the shape-controlled Pt-based nanocatalysts. Finally, we provide an outlook on the potential challenges of shape-controlled nanocatalysts and envision their perspective with suggestions.

Mechanical Interlocking Enhances the Electrocatalytic Oxygen Reduction Activity and Selectivity of Molecular Copper Complexes

Efficient O₂ reduction reaction (ORR) for selective H₂O generation enables advanced fuel cell technology. Nonprecious metal catalysts are viable and attractive alternatives to state-of-the-art Pt-based materials that are expensive. Cu complexes inspired by Cu-containing O₂ reduction enzymes in nature are yet to reach their desired ORR catalytic performance. Here, the concept of mechanical interlocking is introduced to the ligand architecture to enforce dynamic spatial restriction on the Cu coordination site. Interlocked catenane ligands could govern O₂ binding mode, promote electron transfer, and facilitate product elimination. Our results show that ligand interlocking as a catenane steers the ORR selectivity to H₂O as the major product via the 4e^- pathway, rivaling the selectivity of Pt, and boosts the onset potential by 130 mV, the mass activity by 1.8 times, and the turnover frequency by 1.5 fold as compared to the noninterlocked counterpart. Our Cu catenane complex represents one of the first examples to take advantage of mechanical interlocking to afford electrocatalysts with enhanced activity and selectivity. The mechanistic insights gained through this integrated experimental and theoretical study are envisioned to be valuable not just to the area of ORR energy catalysis but also with broad implications on interlocked metal complexes that are of critical importance to the general fields in redox reactions involving proton-coupled electron transfer steps.

Electronic Control of Traditional Iron-Carbon Electrodes to Regulate the Oxygen Reduction Route to Scale Up Water Purification

Shifting four-electron (4e^-) oxygen reduction in fuel cell technology to a two-electron (2e^-) pathway with traditional iron-carbon electrodes is a critical step for hydroxyl radical (HO^•) generation. Here, we fabricated iron-carbon aerogels with desired dimensions (e.g., 40 cm × 40 cm) as working electrodes containing atomic Fe sites and Fe₃C subnanoclusters. Electron-donating Fe₃C provides electrons to FeN₄ through long-range activation for achieving the ideal electronic configuration, thereby optimizing the binding energy of the *OOH intermediate. With an iron-carbon aerogel benefiting from finely tuned electronic density, the selectivity of 2e^- oxygen reduction increased from 10 to 90%. The resultant electrode exhibited unexpectedly efficient HO^• production and fast elimination of organics. Notably, the kinetic constant k_M for sulfamethoxazole (SMX) removal is 60 times higher than that in a traditional iron-carbon electrode. A flow-through pilot device with the iron-carbon aerogel (SA-Fe_0.4NCA) was built to scale up micropolluted water decontamination. The initial total organic carbon (TOC) value of micropolluted water was 4.02 mg L^-1, and it declined and maintained at 2.14 mg L^-1, meeting the standards for drinking water quality in China. Meanwhile, the generation of emerging aromatic nitrogenous disinfection byproducts (chlorophenylacetonitriles) declined by 99.2%, satisfying the public safety of domestic water. This work provides guidance for developing electrochemical technologies to satisfy the flexible and economic demand for water purification, especially in water-scarce areas.

Correlating the orbital overlap area and vibrational frequency shift of an isocyanide moiety adsorbed on Pt and Pd covered Au(111) surfaces

Orbital interactions between adsorbed molecules and the underlying metal surfaces play critical roles in a wide range of surface and interfacial processes. Establishing a correlation between an experimental observable (e.g., vibrational frequency shift of the adsorbed molecule) and the orbital interactions is of vital importance. Herein, theoretical calculations are used to investigate the vibrational frequency shift of phenyl isocyanide molecules as a probe molecule adsorbed on mono- and bi-layer Pt and Pd covered Au(111) surfaces and Pd₂Au₄ and Pt₂Au₄ clusters. By analyzing the density of states (DOS) of the adsorption system, we show that the orbital overlap area of d electronic DOS with a molecular σ or π* orbital, particularly their ratio (R_d-σ/d-π*), can be a meaningful descriptor to explain the frequency shift of the CN moiety. This hypothesis has been verified by simulations for phenyl isocyanide with electron donating NH₂- and withdrawing CF₃- substituent groups, formonitrile and carbon monoxide. Quasi-linear dependence of the frequency shift on R_d-σ/d-π* is observed for both the red and blue shift regions. Our findings build up on previous notions of electronic interactions, which will provide a more quantitative and solid footing to understand and analyze the frequency shift of adsorbed molecules on metal surfaces and the related electronic interactions and catalytic properties.

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. We reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.

d-sp orbital hybridization: a strategy for activity improvement of transition metal catalysts

Orbital hybridization to regulate the electronic structures and surface chemisorption properties of transition metals has been extensively investigated for searching high-performance catalysts toward various reactions. Unlike conventional d-d hybridization, the d-sp hybridization interaction between transition metals and p-block elements could result in surprising electronic properties and catalytic activities. This feature article highlights the recent progress in the development of high-performance transition metal-based catalysts through the extraordinary d-sp hybridization strategy, particularly for energy-related electrocatalytic applications. We start by giving an introduction of fundamental concepts associated with electronic structures of transition metal catalysts, including the Sabatier principle, d-band theory, electronic descriptor, as well as the comparison of d-d hybridization and d-sp hybridization strategies. Then, we summarize the theoretical and experimental advances in d-sp hybridization catalysts, including p-block element-doped metal catalysts, intermetallic catalysts and supported metal catalysts, with emphasis on the important roles of d-sp hybridization in tuning catalytic performances. Finally, we present existing challenges and future development prospects for the rational design of advanced d-sp hybridization catalysts.

Local Coordination Regulation through Tuning Atomic-Scale Cavities of Pd Metallene toward Efficient Oxygen Reduction Electrocatalysis

Moderate adsorption of oxygenated intermediates takes a significant role in rational design of high-efficiency oxygen reduction reaction (ORR) electrocatalysts. Long-serving as a reliable strategy to tune geometric structure of nanomaterials, defect engineering enjoys the great ability of adjusting the coordination environment of catalytic active sites, which enables dominant regulation of adsorption energy and kinetics of ORR catalysis. However, limited to controllable nanocrystals fabrication, inducing uniformly dispersed high-coordinated defects into ultrathin 2D nanosheets remains challenging. Herein, atomic-scale cavities (ASCs) are proposed as a new kind of high-coordinated active site and successfully introduced into suprathin Pd (111)-exposed metallene. Due to its atomic concave architecture, leading to elevated CN and moderately downshifted d-band center, the as-made Pd metallene with ASCs (c-Pd M) exhibits excellent ORR performance with mass activity of 2.76 A mg_Pd ^-1 at 0.9 V versus reversible hydrogen electrode (RHE) and half-wave potential as high as 0.947 V, which is 18.9 (2.7) times higher and 104 (46) mV larger than that of commercial Pt/C (Pd metallene without ASCs). Besides, the durability of c-Pd M exceeds its commercial counterpart with ≈30% loss after 5000 cycles. This work highlights a new-style mentality of designing fancy active sites toward efficient ORR electrocatalysis.

Facet-Defined Strain-Free Spinel Oxide for Oxygen Reduction

Exposing facet and surface strain are critical factors affecting catalytic performance but unraveling the composition-dependent activity on specific facets under strain-controlled environment is still challenging due to the synthetic difficulties. Herein, we achieved a (001) facet-defined Co-Mn spinel oxide surface with different surface compositions using epitaxial growth on Co₃O₄ nanocube template. We adopted composition gradient synthesis to relieve the strain layer by layer, minimizing the surface strain effect on catalytic activity. In this system, experimental and calculational analyses of model oxygen reduction reaction (ORR) activity reveals a volcano-like trend with Mn/Co ratios because of an adequate charge transfer from octahedral-Mn to neighboring Co. Co_0.5Mn_0.5 as an optimized Mn/Co ratio exhibits both outstanding ORR activity (0.894 V vs RHE in 1 M KOH) and stability (2% activity loss against chronoamperometry). By controlling facet and strain, this study provides a well-defined platform for investigating composition-structure-activity relationships in electrocatalytic processes.

Modeling Operando Electrochemical CO2 Reduction

Since the seminal works on the application of density functional theory and the computational hydrogen electrode to electrochemical CO₂ reduction (eCO₂R) and hydrogen evolution (HER), the modeling of both reactions has quickly evolved for the last two decades. Formulation of thermodynamic and kinetic linear scaling relationships for key intermediates on crystalline materials have led to the definition of activity volcano plots, overpotential diagrams, and full exploitation of these theoretical outcomes at laboratory scale. However, recent studies hint at the role of morphological changes and short-lived intermediates in ruling the catalytic performance under operating conditions, further raising the bar for the modeling of electrocatalytic systems. Here, we highlight some novel methodological approaches employed to address eCO₂R and HER reactions. Moving from the atomic scale to the bulk electrolyte, we first show how ab initio and machine learning methodologies can partially reproduce surface reconstruction under operation, thus identifying active sites and reaction mechanisms if coupled with microkinetic modeling. Later, we introduce the potential of density functional theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and extended X-ray absorption fine structure characterization. Next, we review the role of electrolyte and mass transport effects. Finally, we suggest further challenges for computational modeling in the near future as well as our perspective on the directions to follow.

Catalytic resonance of ammonia synthesis by simulated dynamic ruthenium crystal strain

Ammonia affords dense storage for renewable energy as a fungible liquid fuel, provided it can be efficiently synthesized from hydrogen and nitrogen. In this work, the catalysis of ammonia synthesis was computationally explored beyond the Sabatier limit by dynamically straining a ruthenium crystal (±4%) at the resonant frequencies (10² to 10⁵⁺ Hz) of N₂ surface dissociation and hydrogenation. Density functional theory calculations at different strain conditions indicated that the energies of NH_x surface intermediates and transition states scale linearly, allowing the description of ammonia synthesis at a continuum of strain conditions. A microkinetic model including multiple sites and surface diffusion between step and Ru(0001) terrace sites of varying ratios for nanoparticles of differing size revealed that dynamic strain yields catalytic ammonia synthesis conversion and turnover frequency comparable to industrial reactors (400°C, 200 atm) but at lower temperature (320°C) and an order of magnitude lower pressure (20 atm).

Sublayer Stable Fe Dopant in Porous Pd Metallene Boosts Oxygen Reduction Reaction

Engineering the morphology and electronic properties simultaneously of emerging metallene materials is an effective strategy for enhancing their performance as oxygen reduction reaction (ORR) electrocatalysts. Herein, a highly efficient and stable ORR electrocatalyst, Fe-doped ultrathin porous Pd metallene (Fe-Pd UPM) composed of a few layers of 2D atomic metallene layers, was synthesized using a simple one pot wet-chemical method and characterized. Fe-Pd UPM was measured to have enhanced ORR activity compared to undoped Pd metallene. Fe-Pd UPM exhibits a mass activity of 0.736 A mg_Pd^-1 with a loss of mass activity of only 5.1% after 10 000 cycles at 0.9 V versus the reversible hydrogen electrode (vs RHE) in 0.1 M KOH solution. Density functional theory (DFT) calculations reveal that the stable Fe dopant in the inner atomic layers of Fe-Pd UPM delivers a much smaller overpotential during O* hydrogenation into OH*. The morphology, porous structure, and Fe doping were verified to have enhanced ORR activity. We believe that the rational design of metallene materials with porous structures and interlayer doping is promising for the development of efficient and stable electrocatalysts.

MatOpt: A Python Package for Nanomaterials Design Using Discrete Optimization

Novel materials are being enabled by advances in synthesis techniques that achieve ever better control over the atomic-scale structure of materials. The pace of materials development has been further increased by high-throughput computational experiments guided by informatics and machine learning. We have previously demonstrated complementary approaches using mathematical optimization models to search through highly combinatorial design spaces of atomic arrangements, guiding the design of nanostructured materials. In this paper, we highlight the common features of materials optimization problems that can be efficiently modeled via mixed-integer linear optimization models. To take advantage of these commonalities, we have created MatOpt, a Python package that formalizes the process of representing the design space and formulating optimization models for the on-demand design of nanostructured materials. This tool serves to bridge the gap between practitioners with expertise in materials science and those with expertise in formulating and solving mathematical optimization models, effectively lowering the barriers for applying rigorous numerical optimization capabilities during nanostructured materials development.

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

Ultrathin PdAuBiTe Nanosheets as High-Performance Oxygen Reduction Catalysts for a Direct Methanol Fuel Cell Device

Ultrathin 2D metal nanostructures have sparked a lot of research interest because of their improved electrocatalytic properties for fuel cells. So far, no effective technique for preparing ultrathin 2D Pd-based metal nanostructures with more than three compositions has been published. Herein, a new visible-light-induced template technique for producing PdAuBiTe alloyed 2D ultrathin nanosheets is developed. The mass activity of the PdAuBiTe nanosheets against the oxygen reduction reaction (ORR) is 2.48 A mg_Pd ^-1 , which is 27.5/17.7 times that of industrial Pd/C/Pt/C, respectively. After 10 000 potential cyclings, there is no decrease in ORR activity. The PdAuBiTe nanosheets exhibit high methanol tolerance and in situ anti-CO poisoning properties. The PdAuBiTe nanosheets, as cathode electrocatalysts in direct methanol fuel cells, can thus give significant improvement in terms of power density and durability. In O₂ /air, the power density can be increased to 235.7/173.5 mW cm^-2 , higher than that reported in previous work, and which is 2.32/3.59 times higher than Pt/C.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

Solvent-Mediated Shell Dimension Reconstruction of Core@Shell PdAu@Pd Nanocrystals for Robust C1 and C2 Alcohol Electrocatalysis

The core@shell structure dimension of the Pd-based nanocrystals deeply impacts their catalytic properties for C1 and C2 alcohol oxidation reactions. However, the precise simultaneous control on the synthesis of core@shell nanocrystals with different shell dimensions is difficult, and most synthesis on Pd-based core@shell nanocatalysts involves the surfactants participation by multiple steps, thus leads to limited catalytic properties. Herein, for the first time, a facile one-step surfactant-free strategy is developed for shell dimension reconstruction of PdAu@Pd core@shell nanocrystals by altering volume ratios of mixed solvents. The Pd-based sunflower-like (SL) and coral grass-like (CGL) nanocrystals are obtained with different 2D hexagonal nanosheet assembles and 3D network shells, respectively. Benefitting from the clean surface shell of 2D ultrathin nanosheets structure, high atom utilization efficiency, and robust electronic effect. The PdAu@Pd SL achieves the ascendant methanol/ethanol/ethylene glycol oxidation reaction (MOR/EOR/EGOR) activities, much higher than Pd/C catalysts, as well as the improved antipoisoning ability. Notably, this one-step construction shell dimension of PdAu@Pd core@shell catalysts not only provide a significant reference for the improvement of surfactant-free synthetic routes, but also shed light on the advanced engineering on shell dimensions in core@shell nanostructures for electrocatalysis and so forth.

Atomic Regulation of PGM Electrocatalysts for the Oxygen Reduction Reaction

With the increasing enthusiasm for the hydrogen economy and zero-emission fuel cell technologies, intensive efforts have been dedicated to the development of high-performance electrocatalytic materials for the cathodic oxygen reduction reaction (ORR). Some major fundamental breakthroughs have been made in the past few years. Therefore, reviewing the most recent development of platinum-group-metal (PGM) ORR electrocatalysts is of great significance to pushing it forward. It is known that the ORR on the fuel cell electrode is a heterogeneous reaction occurring at the solid/liquid interface, wherein the electron reduces the oxygen along with species in the electrolyte. Therefore, the ORR kinetic is in close correlation with the electronic density of states and wave function, which are dominated by the localized atomic structure including the atomic distance and coordination number (CN). In this review, the recent development in the regulation over the localized state on the catalyst surface is narrowed down to the following structural factors whereby the corresponding strategies include: the crystallographic facet engineering, phase engineering, strain engineering, and defect engineering. Although these strategies show distinctive features, they are not entirely independent, because they all correlate with the atomic local structure. This review will be mainly divided into four parts with critical analyses and comparisons of breakthroughs. Meanwhile, each part is described with some more specific techniques as a methodological guideline. It is hoped that the review will enhance an insightful understanding on PGM catalysts of ORR with a visionary outlook.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

In Situ Surface-Enhanced Raman Spectroscopy Characterization of Electrocatalysis with Different Nanostructures

As energy demands increase, electrocatalysis serves as a vital tool in energy conversion. Elucidating electrocatalytic mechanisms using in situ spectroscopic characterization techniques can provide experimental guidance for preparing high-efficiency electrocatalysts. Surface-enhanced Raman spectroscopy (SERS) can provide rich spectral information for ultratrace surface species and is extremely well suited to studying their activity. To improve the material and morphological universalities, researchers have employed different kinds of nanostructures that have played important roles in the development of SERS technologies. Different strategies, such as so-called borrowing enhancement from shell-isolated modes and shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS)-satellite structures, have been proposed to obtain highly effective Raman enhancement, and these methods make it possible to apply SERS to various electrocatalytic systems. Here, we discuss the development of SERS technology, focusing on its applications in different electrocatalytic reactions (such as oxygen reduction reactions) and at different nanostructure surfaces, and give a brief outlook on its development.

Multi-scale Simulation of Equilibrium Step Fluctuations on Cu(111) Surfaces

Understanding the nature of active sites is a non-trivial task, especially when the catalyst is sensitively affected by chemical reactions and environmental conditions. The challenge lies on capturing explicitly the dynamics of catalyst evolution during reactions. Despite the complexity of catalyst reconstruction, we can untangle them into several elementary processes, of which surface diffusion is of prime importance. By applying density functional theory-kinetic Monte Carlo (DFT-KMC) simulation employed with cluster expansion (CE), we investigated the microscopic mechanism of surface diffusion of Cu with defects such as steps and kinks. Based on the result, the energetics obtained from CE have shown good agreement with DFT calculations. Various diffusion events during the step fluctuations are discussed as well. Aside from the adatom attachment, the diffusion along the step edge is found to be the dominant mass transport mechanism, indicated by the lowest activation energy. We also calculated time correlation functions at 300, 400, and 500 K. However, the time exponent in the correlation function does not strictly follow the power law behavior due to the limited step length, which inhibits variation in the kink density.

A mass-producible integrative structure Pt alloy oxygen reduction catalyst synthesized with atomically dispersive metal-organic framework precursors

Oxygen reduction reaction (ORR) catalyst is one of the most significant influential factors for the application of proton exchange membrane fuel cells. This work introduces a mass-producible high-performance PtZn alloy integrative structure ORR catalyst, synthesized with the atomically dispersive metal-organic framework precursors. This PtZn catalyst displays excellent catalytic activity with the onset reduction potential of 1.0 V_RHE@ 0.16 mA cm^-2 (Reversible hydrogen electrode; RHE) and the half-wave potential of 0.934 V_RHE for the ORR catalysis. The calculated specific activity and mass activity at 0.9 V are 9.44 A m^-2 and 544 A g_Pt^-1, respectively, which are 5.62 times and 5.77 times as high as the commercial Pt/C. The mass activity is remarkably higher than the target put forward by the Department of Energy (DOE; 440 A g_Pt^-1). Furthermore, this PtZn catalyst also exhibits outstanding stability after the 10,000 potential cyclic degeneration test. The ORR current is much higher than Pt/C in the whole potential range not only before but also after the 10,000 potential cycles with identical Pt loading. This catalyst has a multifarious active-site catalytic structure with PtZn alloyed particles and atomically dispersive metal-N active sites on the N-doped graphited carbon matrix, exhibiting appealing ORR catalytic activity and sound stability for the application and scalable production of fuel cell catalysts.

Rationalization of promoted reverse water gas shift reaction by Pt3Ni alloy: Essential contribution from ensemble effect

Bimetallic alloys have attracted considerable attention due to the tunable catalytic activity and selectivity that can be different from those of pure metals. Here, we study the superior catalytic behaviors of the Pt₃Ni nanowire (NW) over each individual, Pt and Ni NWs during the reverse Water Gas Shift (rWGS) reaction, using density functional theory. The results show that the promoted rWGS activity by Pt₃Ni strongly depends on the ensemble effect (a particular arrangement of active sites introduced by alloying), while the contributions from ligand and strain effects, which are of great importance in electrocatalysis, are rather subtle. As a result, a unique Ni-Pt hybrid ensemble is observed at the 110/111 edge of the Pt₃Ni NW, where the synergy between Ni and Pt sites is active enough to stabilize carbon dioxide on the surface readily for the rWGS reaction but moderate enough to allow for the facile removal of carbon monoxide and hydrogenation of hydroxyl species. Our study highlights the importance of the ensemble effect in heterogeneous catalysis of metal alloys, enabling selective binding-tuning and promotion of catalytic activity.

Toward Informed Design of Nanomaterials: A Mechanistic Analysis of Structure-Property-Function Relationships for Faceted Nanoscale Metal Oxides

Nanoscale metal oxides (NMOs) have found wide-scale applicability in a variety of environmental fields, particularly catalysis, gas sensing, and sorption. Facet engineering, or controlled exposure of a particular crystal plane, has been established as an advantageous approach to enabling enhanced functionality of NMOs. However, the underlying mechanisms that give rise to this improved performance are often not systematically examined, leading to an insufficient understanding of NMO facet reactivity. This critical review details the unique electronic and structural characteristics of commonly studied NMO facets and further correlates these characteristics to the principal mechanisms that govern performance in various catalytic, gas sensing, and contaminant removal applications. General trends of facet-dependent behavior are established for each of the NMO compositions, and selected case studies for extensions of facet-dependent behavior, such as mixed metals, mixed-metal oxides, and mixed facets, are discussed. Key conclusions about facet reactivity, confounding variables that tend to obfuscate them, and opportunities to deepen structure-property-function understanding are detailed to encourage rational, informed design of NMOs for the intended application.

Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning

Probe molecule vibrational spectra have a long history of being used to characterize materials including metals, oxides, metal-organic frameworks, and even human proteins. Furthermore, recent advances in machine learning have enabled computationally generated spectra to aid in detailed characterization of complex surfaces with probe molecules. Despite widespread use of probe molecules, the science of probe molecule selection is underdeveloped. Here, we develop physical concepts, including orbital interaction energy and the energy overlap integral, to explain and predict the ability of probe molecules to discriminate structural descriptors. We resolve the crystal orbital overlap population (COOP) to specific molecular orbitals and quantify their bonding character, which directly influences vibrational frequencies. Using only a single adsorbate calculation from density function theory (DFT), we compute the interaction energy of individual adsorbate molecular orbitals with adsorption site atomic orbitals across many different sites. Combining the molecular orbital resolved COOP and changes in orbital interaction energy enables probe molecule selection for improved discrimination of various sites. We demonstrate these concepts by comparing the predicted effectiveness of carbon monoxide (CO), nitric oxide (NO), and ethylene (C₂H₄) to probe Pt adsorption sites. Finally, using a previously developed machine learning framework, we show that models trained on hundreds of thousands of C₂H₄ spectra, computed from DFT, which regress surface binding-type and generalized coordination number, outperform those trained using CO and NO spectra. A python package, pDOS_overlap, for implementing the electron density-based analysis on any combination of adsorbates and materials, is also made available.

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low-energy structures of Pd_n (n = 1-21) clusters supported on CeO₂(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pd_n clusters of any structure. Low-energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters, and large cluster isomers share similarities in both shape and energy. The findings elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)-based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening

The binding site and energy is an invaluable descriptor in high-throughput screening of catalysts, as it is accessible and correlates with the activity and selectivity. Recently, comprehensive binding energy prediction machine-learning models have been demonstrated and promise to accelerate the catalyst screening. Here, we present a simple and versatile representation, applicable to any deep-learning models, to further accelerate such process. Our approach involves labeling the binding site atoms of the unrelaxed bare surface geometry; hence, for the model application, density functional theory calculations can be completely removed if the optimized bulk structure is available as is the case when using the Materials Project database. In addition, we present ensemble learning, where a set of predictions is used together to form a predictive distribution that reduces the model bias. We apply the labeled site approach and ensemble to crystal graph convolutional neural network and the ∼40 000 data set of alloy catalysts for CO₂ reduction. The proposed model applied to the data set of unrelaxed structures shows 0.116 and 0.085 eV mean absolute error, respectively, for CO and H binding energy, better than the best method (0.13 and 0.13 eV) in the literature that requires costly geometry relaxations. The analysis of the model parameters demonstrates that the model can effectively learn the chemical information related to the binding site.

Bimetallic PtAu electrocatalysts for the oxygen reduction reaction: challenges and opportunities

Highly active, durable oxygen reduction reaction (ORR) electrocatalysts have an essential role in promoting the continuous operation of advanced energy technologies such as fuel cells and metal-air batteries. Considering the scarce reserve of Pt and its unsatisfactory overall performance, there is an urgent demand for the development of new generation ORR electrocatalysts that are substantially better than the state-of-the-art supported Pt-based nanocatalysts, such as Pt/C. Among various nanostructures, bimetallic PtAu represents one unique alloy system where highly contradictory performance has been reported. While it is generally accepted that Au may contribute to stabilizing Pt, its role in modulating the intrinsic activity of Pt remains unclear. This perspective will discuss critical structural issues that affect the intrinsic ORR activities of bimetallic PtAu, with an eye on elucidating the origin of seemingly inconsistent experimental results from the literature. As a relatively new class of electrodes, we will also highlight the performance of dealloyed nanoporous gold (NPG) based electrocatalysts, which allow a unique combination of structural properties highly desired for this important reaction. Finally, we will put forward the challenges and opportunities for the incorporation of these advanced electrocatalysts into membrane electrode assemblies (MEA) for actual fuel cells.

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

Predicting metal-metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities

Strain-engineering of bimetallic nanomaterials is an important design strategy for developing new catalysts. Herein, we introduce an approach for including strain effects into a recently introduced, density functional theory (DFT)-based alloy stability model. The model predicts adsorption site stabilities in nanoparticles and connects these site stabilities with catalytic reactivity and selectivity. Strain-based dependencies will increase the model's accuracy for nanoparticles affected by finite-size effects. In addition to the stability of small nanoparticles, strain also influences the heat of adsorption of epitaxially grown metal-on-metal adlayers. In this respect, we successfully benchmark the strain-including alloy stability model with previous experimentally determined trends in the heats of adsorption of Au and Cu adlayers on Pt (111). For these systems, our model predicts stronger bimetallic interactions in the first monolayer than monometallic interactions in the second monolayer. We explicitly quantify the interplay between destabilizing strain effects and the energy gained by forming new metal-metal bonds. While tensile strain in the first Cu monolayer significantly destabilizes the adsorption strength, compressive strain in the first Au monolayer has a minimal impact on the heat of adsorption. Hence, this study introduces and, by comparison with previous experiments, validates an efficient DFT-based approach for strain-engineering the stability, and, in turn, the catalytic performance, of active sites in bimetallic alloys with atomic level resolution.

Preserving the Exposed Facets of Pt3Sn Intermetallic Nanocubes During an Order to Disorder Transition Allows the Elucidation of the Effect of the Degree of Alloy Ordering on Electrocatalysis

Controlling which facets are exposed in nanocrystals is crucial to understanding different activity between ordered and disordered alloy electrocatalysts. We modify the degree of ordering of Pt₃Sn nanocubes, while maintaining the shape and size, to enable a direct evaluation of the effect of the order on ORR catalytic activity. We demonstrate a 2.3-fold enhancement in specific activity by 60- and 30%-ordered Pt₃Sn nanocubes compared to 95%-ordered. This was shown to be likely due to surface vacancies in the less-ordered particles. The greater order, however, results in higher stability of the electrocatalyst, with the more disordered nanoparticles showing the dissolution of tin and platinum species during electrocatalysis.

Bonding–antibonding state transition induces multiple electron modulations toward oxygen reduction reaction electrocatalysis

B doping induces the transformation from the bonding state to the antibonding state of an Ni–N bond, resulting in enhanced ORR activity.

Revealing the nature of active sites in electrocatalysis

Heterogeneous electrocatalysis plays a central role in the development of sustainable, carbon-neutral pathways for energy provision and the production of various chemicals. It determines the overall efficiency of electrochemical devices that involve catalysis at the electrode/electrolyte interface. In this perspective, we discuss key aspects for the identification of active centers at the surface of electrocatalysts and important factors that influence them. The role of the surface structure, nanoparticle shape/size and the electrolyte composition in the resulting catalytic performance is of particular interest in this work. We highlight challenges that from our point of view need to be tackled, and provide guidelines for the design of "real life" electrocatalysts for renewable energy provision systems as well as for the production of industrially important compounds.