Deciphering the Structural and Chemical Transformations of Oxide Catalysts during Oxygen Evolution Reaction Using Quick X-ray Absorption Spectroscopy and Machine Learning

Empowering Generalist Material Intelligence with Large Language Models

Large language models (LLMs) are steering the development of generalist materials intelligence (GMI), a unified framework integrating conceptual reasoning, computational modeling, and experimental validation. Central to this framework is the agent-in-the-loop paradigm, where LLM-based agents function as dynamic orchestrators, synthesizing multimodal knowledge, specialized models, and experimental robotics to enable fully autonomous discovery. Drawing from a comprehensive review of LLMs' transformative impact across representative applications in materials science, including data extraction, property prediction, structure generation, synthesis planning, and self-driven labs, this study underscores how LLMs are revolutionizing traditional tasks, catalyzing the agent-in-the-loop paradigm, and bridging the ontology-concept-computation-experiment continuum. Then the unique challenges of scaling up LLM adoption are discussed, particularly those arising from the misalignment of foundation LLMs with materials-specific knowledge, emphasizing the need to enhance adaptability, efficiency, sustainability, interpretability, and trustworthiness in the pursuit of GMI. Nonetheless, it is important to recognize that LLMs are not universally efficient. Their substantial resource demands and inconsistent performance call for careful deployment based on demonstrated task suitability. To address these realities, actionable strategies and a progressive roadmap for equitably and democratically implementing materials-aware LLMs in real-world practices are proposed.

A combined soft X-ray and theoretical investigation discloses the water harvesting behaviour of Mg-MOF-74 at the crystal surface

Metal-organic frameworks (MOFs) are receiving growing interest as transformative materials for real-world atmospheric water harvesting applications. However, obtaining molecular-level details on how surface effects regulate MOF water uptake has proven to be elusive. Here, we present a novel methodology based on ambient pressure soft X-ray absorption spectroscopy (AP-NEXAFS), machine learning-assisted theoretical spectroscopy and molecular dynamics simulations to gain selective insights into the behaviour of water at a MOF crystal surface. We applied our interdisciplinary method to investigate the structural and dynamical properties of water at the surface of the Mg-MOF-74 system, while obtaining complementary information on the water uptake and release from the bulk by synchrotron powder X-ray diffraction. Our investigation pointed out the simultaneous presence of Mg open sites and residual gas-phase water during dehydration, and proved that during water release a high number of surface Mg sites still interact with one or two water molecules. Conversely, when looking at the bulk, a significantly lower number of Mg sites have been found to interact with water molecules in the same experimental conditions. This behaviour suggests that the water adsorption (desorption) process starts from the interior of the material and propagates towards the channel openings. The combined approach based on AP-NEXAFS, PXRD experimental determinations and ML-supported theoretical analyses has been found to be a valuable tool to provide a thorough description of the water harvesting process at both surface and bulk of the crystal.

Guided electrocatalyst design through in-situ techniques and data mining approaches

Intuitive design strategies, primarily based on literature research and trial-and-error efforts, have significantly contributed to advancements in the electrocatalyst field. However, the inherently time-consuming and inconsistent nature of these methods presents substantial challenges in accelerating the discovery of high-performance electrocatalysts. To this end, guided design approaches, including in-situ experimental techniques and data mining, have emerged as powerful catalyst design and optimization tools. The former offers valuable insights into the reaction mechanisms, while the latter identifies patterns within large catalyst databases. In this review, we first present the examples using in-situ experimental techniques, emphasizing a detailed analysis of their strengths and limitations. Then, we explore advancements in data-mining-driven catalyst development, highlighting how data-driven approaches complement experimental methods to accelerate the discovery and optimization of high-performance catalysts. Finally, we discuss the current challenges and possible solutions for guided catalyst design. This review aims to provide a comprehensive understanding of current methodologies and inspire future innovations in electrocatalytic research.

Fluorine Engineering Induces Phase Transformation in NiCo2O4 for Enhanced Active Motifs Formation in Oxygen Evolution Reaction

Dynamic reconstruction of catalysts is key to active site formation in alkaline oxygen evolution reaction (OER), but precise control over this process remains challenging. Herein, F-doped NiCo2O4 (NiCo2O4-Fn), consisting of a NiCo2O4 core and a (NH4)NixCo1-xF3 shell is reported, which promotes the formation of a dual-metal NiCoOOH active phase. In situ Raman and X-ray absorption fine structure analyses reveal that the NiCoOOH, rich in oxygen vacancies (Ov), forms at 1.2 V versus the reversible hydrogen electrode (RHE) for NiCo2O4-F1, in contrast to the NiOOH phase formation at 1.4 V versus RHE for undoped NiCo2O4. This is facilitated by the transformation of (NH4)NixCo1-xF3 into amorphous NixCo1-x(OH)2 in the KOH electrolyte without bias. Electrochemical tests show that NiCo2O4-F1 exhibits a 14-fold increase in intrinsic activity compared to NiCo2O4. Theoretical calculations suggest that Ov-induced unsaturated Co and Ni sites enhance electroactivity by promoting *OH intermediates adsorption and conversion, lowering the OER energy barrier. The oriented control of NiCoOOH active motifs in NiCo2O4 spinel, achieved through fluorine engineering, paves a new avenue for designing efficient OER electrocatalysts.

Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data

Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical classification was demonstrated by extracting IR spectral images from the Spectral Database for Organic Compounds and converting them into 208,620-dimensional vector data. Hierarchical clustering of 230 compounds revealed distinct main clusters (A-G), each with specific subclusters exhibiting higher intracluster similarities. Despite the challenges, including sensitivity to spectral deviations and difficulty of distinguishing delicate chemical structures in spectra with low transparency in the fingerprint area, the proposed image recognition approach exhibits good potential. Both principal component analysis and k-means clustering produced similar results. Furthermore, the method demonstrated high robustness to noise. The Tanimoto coefficient was used to evaluate the molecular similarity, providing valuable insights. However, some results deviated from chemists' intuitions. The study also highlighted that the scaling composition formulas and molecular weights did not affect the classification results because high-dimensional features dominated the process. A comparison of the clustering results obtained from molecular fingerprints, using the adjusted Rand index as a metric, indicated that the image data provided better classification performance than numerical data of the same resolution. Overall, this study demonstrates the feasibility of using machine learning with IR spectral image data for chemical classification and offers a novel perspective that complements traditional methods, although the classifications may not always align with chemists' intuitions. This approach has broader implications for fields such as drug discovery, materials science, and automated spectral analysis, where handling large, raw spectral data sets is essential.

A Combined X-ray Absorption and UV-Vis Spectroscopic Study of the Iron-Catalyzed Belousov-Zhabotinsky Reaction

The iron-catalyzed Belousov-Zhabotinsky (BZ) oscillating reaction was investigated in an unstirred reactor by combining Br K-edge X-ray absorption and UV-vis spectroscopies. The experimental data were analyzed through an integrated approach based on principal component analysis, multivariate curve resolution, and ab initio theoretical X-ray absorption spectroscopy (XAS), providing quantitative insights into the properties of the key reaction bromine species while contextually tracking the Fe2+ to Fe3+ oscillatory transformation. The high-quality XAS experimental data supported by the multivariate and theoretical analyses provide clear-cut evidence of the conversion of bromate, initially predominant in the reaction mixture, to the brominated derivative of the employed allylmalonic acid substrate. The described interdisciplinary method was proven to be valuable to monitor the fate of the main BZ reaction brominated species, which are silent to conventional spectroscopic methods of detection, and the developed approach may support future mechanistic investigations of other oscillatory systems.

An Intelligent Prediction Model for the Synthesis Conditions of Metal-Organic Frameworks Utilizing Artificial Neural Networks Enhanced by Genetic Algorithm Optimization

In the field of emerging materials, metal-organic frameworks (MOFs) have gained prominence due to their unique porous structures, showing versatility in gas adsorption, storage, separation, and liquid processes. However, their decomposition, collapse tendencies, and complex synthesis make large-scale production costly and challenging with no accurate method for predicting synthesis conditions. This work proposes an intelligent prediction model based on the structural characteristics of MOFs to forecast synthesis conditions. A genetic algorithm-optimized back-propagation (BP) neural network was developed, starting with feature selection via the minimum redundancy maximum relevance algorithm to rank feature importance. The optimal number of inputs and outputs was determined on the basis of performance, followed by genetic algorithm optimization of the BP neural network. The best initial population size and number of hidden nodes were identified. The study compared 10 models, including a genetic algorithm-optimized BP neural network and a simple BP neural network. The results revealed that the R coefficient of the optimized model reached 96.2%, surpassing that of conventional methods with all R values of approximately 85%. This approach allows for accurate prediction of MOF synthesis conditions, aiding material manufacturing in precise control over synthesis processes, improving material quality, and reducing raw material waste.

Operando monitoring of the functional role of tetrahedral cobalt centers for the oxygen evolution reaction

The complexity of the intrinsic oxygen evolution reaction (OER) mechanism, particularly the precise relationships between the local coordination geometry of active metal centers and the resulting OER kinetics, remains to be fully understood. Herein, we construct a series of 3 d transition metal-incorporated cobalt hydroxide-based nanobox architectures for the OER which contain tetrahedrally coordinated Co(II) centers. Combination of bulk- and surface-sensitive operando spectroelectrochemical approaches reveals that tetrahedral Co(II) centers undergo a dynamic transformation into highly active Co(IV) intermediates acting as the true OER active species which activate lattice oxygen during the OER. Such a dynamic change in the local coordination geometry of Co centers can be further facilitated by partial Fe incorporation. In comparison, the formation of such active Co(IV) species is found to be hindered in CoOOH and Co-FeOOH, which are predominantly containing [CoIIIO6] and [CoII/FeIIIO6] octahedra, respectively, but no mono-μ-oxo-bridged [CoIIO4] moieties. This study offers a comprehensive view of the dynamic role of local coordination geometry of active metal centers in the OER kinetics.

Lanthanum-Promoted Electrocatalyst for the Oxygen Evolution Reaction: Unique Catalyst or Oxide Deconstruction?

A conventional performance metric for electrocatalysts that promote the oxygen evolution reaction (OER) is the current density at a given overpotential. However, the assumption that increased current density at lower overpotentials indicates superior catalyst design is precarious for OER catalysts in the working environment, as the crystalline lattice is prone to deconstruction and amorphization, thus greatly increasing the concentration of catalytic active sites. We show this to be the case for La3+ incorporation into Co3O4. Powder X-ray diffraction (PXRD), Raman spectroscopy and extended X-ray absorption fine structure (EXAFS) reveal smaller domain sizes with decreased long-range order and increased amorphization for La-modified Co3O4. This lattice deconstruction is exacerbated under the conditions of OER as indicated by operando spectroscopies. The overpotential for OER decreases with increasing La3+ concentration, with maximum activity achieved at 17% La incorporation. HRTEM images and electron diffraction patterns clearly show the formation of an amorphous overlayer during OER catalysis that is accelerated with La3+ addition. O 1s XPS spectra after OER show the loss of lattice-oxide and an increase in peak intensities associated with hydroxylated or defective O-atom environments, consistent with Co(O)x(OH)y species in an amorphous overlayer. Our results suggest that improved catalytic activity of oxides incorporated with La3+ ions (and likely other metal ions) is due to an increase in the number of terminal octahedral Co(O)x(OH)y edge sites upon Co3O4 lattice deconstruction, rather than enhanced intrinsic catalysis.

Bamboo-Like Carbon Nanotube-Encapsulated Fe2C Nanoparticles Activate Confined Fe2O3 Nanoclusters Via d-p-d Orbital Coupling for Alkaline Oxygen Evolution Reaction

The efficient anion exchange membrane water electrolysis is challenging with low cell voltage and long-term stability at large current density, due to the unstable anodic oxygen evolution reaction (OER). Fe-based electrocatalysts are potential candidates for the anodic OER. In Fe-based materials, iron oxides always show better stability in alkaline solution but lower OER activity. However, the catalysts in previous study are difficult to continuously and effectively activate iron oxides supported on carbon during electrocatalysis. Herein, a new class of electrocatalyst: bamboo-like carbon nanotubes (B-CNT)-encapsulated Fe2C nanoparticles (NPs) supported Fe2O3 nanoclusters (NCs), named Fe2O3/B-CNT@Fe2C is reported. Theoretical calculations and experimental results reveal that B-CNT-encapsulate Fe2C NPs activate Fe2O3 NCs by the d-p-d orbital coupling, thereby weakening the adsorption of OOH* intermediate during OER process. The electrolyzer based on the electrocatalyst requires only 1.48 V to reach 1.0 A cm-2 and shows a long-term stability at 1.0 A cm-2 for 1600 h, comparable to the best-reported values for the anion exchange membrane water electrolyzer (AEMWE).

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

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.

Nanostructured Fe-Doped Ni3S2 Electrocatalyst for the Oxygen Evolution Reaction with High Stability at an Industrially-Relevant Current Density

A novel oxygen evolution reaction (OER) electrocatalyst was prepared by a synthesis strategy consisting of the solvothermal growth of Ni3S2 nanostructures on Ni foam, followed by hydrothermal incorporation of Fe species (Fe-Ni3S2/Ni foam). This electrocatalyst displayed a low OER overpotential of 230 mV at 100 mA·cm-2, a low Tafel slope of 43 mV·dec-1, and constant performance at an industrially relevant current density (500 mA·cm-2) over 100 h in a 1.0 M KOH electrolyte, despite a minor loss of Fe in the process. Based on a detailed characterization by (in situ) Raman spectroscopy, (quasi-in situ) XPS, SEM, TEM, XRD, ICP-AES, EIS, and Cdl analysis, the high OER activity and stability of Fe-Ni3S2/Ni foam were attributed to the nanostructuring of the surface in the form of stable nanosheets and to the combination of Ni3S2 granting suitable electrical conductivity with newly formed NiFe-based (oxy)hydroxides at the surface of the material providing the active sites for OER.

Unraveling the Role of the Stoichiometry of Atomic Layer Deposited Nickel Cobalt Oxides on the Oxygen Evolution Reaction

Nickel cobalt oxides (NCOs) are promising, non-precious oxygen evolution reaction (OER) electrocatalysts. However, the stoichiometry-dependent electrochemical behavior makes it crucial to understand the structure-OER relationship. In this work, NCO thin film model systems are prepared using atomic layer deposition. In-depth film characterization shows the phase transition from Ni-rich rock-salt films to Co-rich spinel films. Electrochemical analysis in 1 m KOH reveals a synergistic effect between Co and Ni with optimal performance for the 30 at.% Co film after 500 CV cycles. Electrochemical activation correlates with film composition, specifically increasing activation is observed for more Ni-rich films as its bulk transitions to the active (oxy)hydroxide phase. In parallel to this transition, the electrochemical surface area (ECSA) increases up to a factor 8. Using an original approach, the changes in ECSA are decoupled from intrinsic OER activity, leading to the conclusion that 70 at.% Co spinel phase NCO films are intrinsically the most active. The studies point to a chemical composition dependent OER mechanism: Co-rich spinel films show instantly high activities, while the more sustainable Ni-rich rock-salt films require extended activation to increase the ECSA and OER performance. The results highlight the added value of working with model systems to disclose structure-performance mechanisms.

Reversible metal cluster formation on Nitrogen-doped carbon controlling electrocatalyst particle size with subnanometer accuracy

Copper and nitrogen co-doped carbon catalysts exhibit a remarkable behavior during the electrocatalytic CO2 reduction (CO2RR), namely, the formation of metal nanoparticles from Cu single atoms, and their subsequent reversible redispersion. Here we show that the switchable nature of these species holds the key for the on-demand control over the distribution of CO2RR products, a lack of which has thus far hindered the wide-spread practical adoption of CO2RR. By intermitting pulses of a working cathodic potential with pulses of anodic potential, we were able to achieve a controlled fragmentation of the Cu particles and partial regeneration of single atom sites. By tuning the pulse durations, and by tracking the catalyst's evolution using operando quick X-ray absorption spectroscopy, the speciation of the catalyst can be steered toward single atom sites, ultrasmall metal clusters or large metal nanoparticles, each exhibiting unique CO2RR functionalities.

Coupled X-ray Absorption/UV-vis Monitoring of a Prototypical Oscillating Reaction

Oscillating reactions are among the most intriguing phenomena in chemistry, but many questions on their mechanisms still remain unanswered, due to their intrinsic complexity and to the low sensitivity of the most common spectroscopic techniques toward the reaction brominated species. In this work, we investigate the cerium ion-catalyzed Belousov-Zhabotinsky (BZ) oscillating reaction by means of time-resolved X-ray absorption spectroscopy (XAS), in combination with UV-vis spectroscopy and unsupervised machine learning, multivariate curve resolution, and kinetic analyses. Altogether, we provide new insights into the collective oscillatory behavior of the key brominated species involved in the classical BZ reaction and measure previously unreported oscillations in their concentrations through Br K-edge XAS, while simultaneously tracking the oscillatory Ce4+-to-Ce3+ transformation by coupling XAS with UV-vis spectroscopy. Our work evidences the potential of the XAS technique to investigate the mechanisms of oscillatory chemical systems whose species are often not detectable with conventional experimental methods.

Recent Advances in Iridium-based Electrocatalysts for Acidic Electrolyte Oxidation

Ongoing research to develop advanced electrocatalysts for the oxygen evolution reaction (OER) is needed to address demand for efficient energy conversion and carbon-free energy sources. In the OER process, acidic electrolytes have higher proton concentration and faster response than alkaline ones, but their harsh strongly acidic environment requires catalysts with greater corrosion and oxidation resistance. At present, iridium oxide (IrO2) with its strong stability and excellent catalytic performance is the catalyst of choice for the anode side of commercial PEM electrolysis cells. However, the scarcity and high cost of iridium (Ir) and the unsatisfactory activity of IrO2 hinder industrial scale application and the sustainable development of acidic OER catalytic technology. This highlights the importance of further research on acidic Ir-based OER catalysts. In this review, recent advances in Ir-based acidic OER electrocatalysts are summarized, including fundamental understanding of the acidic OER mechanism, recent insights into the stability of acidic OER catalysts, highly efficient Ir-based electrocatalysts, and common strategies for optimizing Ir-based catalysts. The future challenges and prospects of developing highly effective Ir-based catalysts are also discussed.

Synergizing Fe2O3 Nanoparticles on Single Atom Fe-N-C for Nitrate Reduction to Ammonia at Industrial Current Densities

The electrochemical reduction of nitrates (NO3 -) enables a pathway for the carbon neutral synthesis of ammonia (NH3), via the nitrate reduction reaction (NO3RR), which has been demonstrated at high selectivity. However, to make NH3 synthesis cost-competitive with current technologies, high NH3 partial current densities (jNH3) must be achieved to reduce the levelized cost of NH3. Here, the high NO3RR activity of Fe-based materials is leveraged to synthesize a novel active particle-active support system with Fe2O3 nanoparticles supported on atomically dispersed Fe-N-C. The optimized 3×Fe2O3/Fe-N-C catalyst demonstrates an ultrahigh NO3RR activity, reaching a maximum jNH3 of 1.95 A cm-2 at a Faradaic efficiency (FE) for NH3 of 100% and an NH3 yield rate over 9 mmol hr-1 cm-2. Operando XANES and post-mortem XPS reveal the importance of a pre-reduction activation step, reducing the surface Fe2O3 (Fe3+) to highly active Fe0 sites, which are maintained during electrolysis. Durability studies demonstrate the robustness of both the Fe2O3 particles and Fe-Nx sites at highly cathodic potentials, maintaining a current of -1.3 A cm-2 over 24 hours. This work exhibits an effective and durable active particle-active support system enhancing the performance of the NO3RR, enabling industrially relevant current densities and near 100% selectivity.

Stabilizing the oxidation state of catalysts for effective electrochemical carbon dioxide conversion

In the electrocatalytic CO2 reduction reaction (CO2RR), metal catalysts with an oxidation state generally demonstrate more favorable catalytic activity and selectivity than their corresponding metallic counterparts. However, the persistence of oxidative metal sites under reductive potentials is challenging since the transition to metallic states inevitably leads to catalytic degradation. Herein, a thorough review of research on oxidation-state stabilization in the CO2RR is presented, starting from fundamental concepts and highlighting the importance of oxidation state stabilization while revealing the relevance of dynamic oxidation states in product distribution. Subsequently, the functional mechanisms of various oxidation-state protection strategies are explained in detail, and in situ detection techniques are discussed. Finally, the prevailing and prospective challenges associated with oxidation-state protection research are discussed, identifying innovative opportunities for mechanistic insights, technology upgrades, and industrial platforms to enable the commercialization of the CO2RR.

Oxygen Radical Coupling on Short-Range Ordered Ru Atom Arrays Enables Exceptional Activity and Stability for Acidic Water Oxidation

The discovery of efficient and stable electrocatalysts for oxygen evolution reaction (OER) in acid is vital for the commercialization of the proton-exchange membrane water electrolyzer. In this work, we demonstrate that short-range Ru atom arrays with near-ideal Ru-Ru interatomic distances and a unique Ru-O hybridization state can trigger direct O*-O* radical coupling to form an intermediate O*-O*-Ru configuration during acidic OER without generating OOH* species. Further, the Ru atom arrays suppress the participation of lattice oxygen in the OER and the dissolution of active Ru. Benefiting from these advantages, the as-designed Ru array-Co3O4 electrocatalyst breaks the activity/stability trade-off that plagues RuO2-based electrocatalysts, delivering an excellent OER overpotential of only 160 mV at 10 mA cm-2 in 0.5 M H2SO4 and outstanding durability during 1500 h operation, representing one of the best acid-stable OER electrocatalysts reported to date. 18O-labeled operando spectroscopic measurements together with theoretical investigations revealed that the short-range Ru atom arrays switched on an oxide path mechanism (OPM) during the OER. Our work not only guides the design of improved acidic OER catalysts but also encourages the pursuit of short-range metal atom array-based electrocatalysts for other electrocatalytic reactions.

Deciphering Photoinduced Catalytic Reaction Mechanisms in Natural and Artificial Photosynthetic Systems on Multiple Temporal and Spatial Scales Using X-ray Probes

Utilization of renewable energies for catalytically generating value-added chemicals is highly desirable in this era of rising energy demands and climate change impacts. Artificial photosynthetic systems or photocatalysts utilize light to convert abundant CO2, H2O, and O2 to fuels, such as carbohydrates and hydrogen, thus converting light energy to storable chemical resources. The emergence of intense X-ray pulses from synchrotrons, ultrafast X-ray pulses from X-ray free electron lasers, and table-top laser-driven sources over the past decades opens new frontiers in deciphering photoinduced catalytic reaction mechanisms on the multiple temporal and spatial scales. Operando X-ray spectroscopic methods offer a new set of electronic transitions in probing the oxidation states, coordinating geometry, and spin states of the metal catalytic center and photosensitizers with unprecedented energy and time resolution. Operando X-ray scattering methods enable previously elusive reaction steps to be characterized on different length scales and time scales. The methodological progress and their application examples collected in this review will offer a glimpse into the accomplishments and current state in deciphering reaction mechanisms for both natural and synthetic systems. Looking forward, there are still many challenges and opportunities at the frontier of catalytic research that will require further advancement of the characterization techniques.

Spatially and Temporally Resolved Dynamic Response of Co-Based Composite Interface during the Oxygen Evolution Reaction

Interfacial interaction dictates the overall catalytic performance and catalytic behavior rules of the composite catalyst. However, understanding of interfacial active sites at the microscopic scale is still limited. Importantly, identifying the dynamic action mechanism of the "real" active site at the interface necessitates nanoscale, high spatial-time-resolved complementary-operando techniques. In this work, a Co3O4 homojunction with a well-defined interface effect is developed as a model system to explore the spatial-correlation dynamic response of the interface toward oxygen evolution reaction. Quasi in situ scanning transmission electron microscopy-electron energy-loss spectroscopy with high spatial resolution visually confirms the size characteristics of the interface effect in the spatial dimension, showing that the activation of active sites originates from strong interfacial electron interactions at a scale of 3 nm. Multiple time-resolved operando spectroscopy techniques explicitly capture dynamic changes in the adsorption behavior for key reaction intermediates. Combined with density functional theory calculations, we reveal that the dynamic adjustment of multiple adsorption configurations of intermediates by highly activated active sites at the interface facilitates the O-O coupling and *OOH deprotonation processes. The dual dynamic regulation mechanism accelerates the kinetics of oxygen evolution and serves as a pivotal factor in promoting the oxygen evolution activity of the composite structure. The resulting composite catalyst (Co-B@Co3O4/Co3O4 NSs) exhibits an approximately 70-fold turnover frequency and 20-fold mass activity than the monomer structure (Co3O4 NSs) and leads to significant activity (η10 ∼257 mV). The visual complementary analysis of multimodal operando/in situ techniques provides us with a powerful platform to advance our fundamental understanding of interfacial structure-activity relationships in composite structured catalysts.

Role of Fe decoration on the oxygen evolving state of Co3O4 nanocatalysts

The production of green hydrogen through alkaline water electrolysis is the key technology for the future carbon-neutral industry. Nanocrystalline Co3O4 catalysts are highly promising electrocatalysts for the oxygen evolution reaction and their activity strongly benefits from Fe surface decoration. However, limited knowledge of decisive catalyst motifs at the atomic level during oxygen evolution prevents their knowledge-driven optimization. Here, we employ a variety of operando spectroscopic methods to unveil how Fe decoration increases the catalytic activity of Co3O4 nanocatalysts as well as steer the (near-surface) active state formation. Our study shows a link of the termination-dependent Fe decoration to the activity enhancement and a significantly stronger Co3O4 near-surface (structural) adaptation under the reaction conditions. The near-surface Fe- and Co-O species accumulate an oxidative charge and undergo a reversible bond contraction during the catalytic process. Moreover, our work demonstrates the importance of low coordination surface sites on the Co3O4 host to ensure an efficient Fe-induced activity enhancement, providing another puzzle piece to facilitate optimized catalyst design.

Surface engineering for stable electrocatalysis

In recent decades, significant progress has been achieved in rational developments of electrocatalysts through constructing novel atomistic structures and modulating catalytic surface topography, realizing substantial enhancement in electrocatalytic activities. Numerous advanced catalysts were developed for electrochemical energy conversion, exhibiting low overpotential, high intrinsic activity, and selectivity. Yet, maintaining the high catalytic performance under working conditions with high polarization and vigorous microkinetics that induce intensive degradation of surface nanostructures presents a significant challenge for commercial applications. Recently, advanced operando and computational techniques have provided comprehensive mechanistic insights into the degradation of surficial functional structures. Additionally, various innovative strategies have been devised and proven effective in sustaining electrocatalytic activity under harsh operating conditions. This review aims to discuss the most recent understanding of the degradation microkinetics of catalysts across an entire range of anodic to cathodic polarizations, encompassing processes such as oxygen evolution and reduction, hydrogen reduction, and carbon dioxide reduction. Subsequently, innovative strategies adopted to stabilize the materials' structure and activity are highlighted with an in-depth discussion of the underlying rationale. Finally, we present conclusions and perspectives regarding future research and development. By identifying the research gaps, this review aims to inspire further exploration of surface degradation mechanisms and rational design of durable electrocatalysts, ultimately contributing to the large-scale utilization of electroconversion technologies.

Artificial neural network for deciphering the structural transformation of condensed ZnO by extended x-ray absorption fine structure spectroscopy

Pressure-induced structural phase transitions play a pivotal role in unlocking novel material functionalities and facilitating innovations in materials science. Nonetheless, unveiling the mechanisms of densification, which relies heavily on precise and comprehensive structural analysis, remains a challenge. Herein, we investigated the archetypalB4 →B1 phase transition pathway in ZnO by combining x-ray absorption fine structure (XAFS) spectroscopy with machine learning. Specifically, we developed an artificial neural network (NN) to decipher the extended-XAFS spectra by reconstructing the partial radial distribution functions of Zn-O/Zn pairs. This provided us with access to the evolution of the structural statistics for all the coordination shells in condensed ZnO, enabling us to accurately track the changes in the internal structural parameteruand the anharmonic effect. We observed a clear decrease inuand an increased anharmonicity near the onset of theB4 →B1 phase transition, indicating a preference for the iT phase as the intermediate state to initiate the phase transition that can arise from the softening of shear phonon modes. This study suggests that NN-based approach can facilitate a more comprehensive and efficient interpretation of XAFS under complexin-situconditions, which paves the way for highly automated data processing pipelines for high-throughput and real-time characterizations in next-generation synchrotron photon sources.

Activating two-dimensional semiconductors for photocatalysis: a cross-dimensional strategy

The emerging two-dimensional (2D) semiconductors substantially extend materials bases for versatile applications such as semiconductor photocatalysis demanding semiconductive matrices and large surface areas. The dimensionality, while endowing 2D semiconductors the unique properties to host photocatalytic functionality of pollutant removal and hydrogen evolution, hurdles the activation paths to form heterogenous photocatalysts where the photochemical processes are normally superior over these on the mono-compositional counterparts. In this perspective, we present a cross-dimensional strategy to employ thenD (n= 0-2) clusters or nanomaterials as activation partners to boost the photocatalytic activities of the 2D semiconductors. The formation principles of heterogenous photocatalysts are illustrated specifically for the 2D matrices, followed by selection criteria of them among the vast 2D database. The computer investigations are illustrated in the density functional theory route and machine learning benefitted from the vast samples in the 2D library. Synthetic realizations and characterizations of the 2D heterogenous systems are introduced with an emphasis on chemical methods and advanced techniques to understand materials and mechanistic studies. The perspective outlooks cross-dimensional activation strategies of the 2D materials for other applications such as CO2removal, and materials matrices in other dimensions which may inspire incoming research within these fields.

Distortion-Induced Interfacial Charge Transfer at Single Cobalt Atom Secured on Ordered Intermetallic Surface Enhances Pure Oxygen Production

In this work, atomic cobalt (Co) incorporation into the Pd2Ge intermetallic lattice facilitates operando generation of a thin layer of CoO over Co-substituted Pd2Ge, with Co in the CoO surface layer functioning as single metal sites. Hence the catalyst has been titled Co1-CoO-Pd2Ge. High-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, and X-ray absorption spectroscopy confirm the existence of CoO, with some of the Co bonded to Ge by substitution of Pd sites in the Pd2Ge lattice. The role of the CoO layer in the oxygen evolution reaction (OER) has been verified by its selective removal using argon sputtering and conducting the OER on the etched catalyst. In situ X-ray absorption near-edge structure and extended X-ray absorption fine structure spectroscopy demonstrate that CoO gets transformed to CoOOH (Co3+) in operando condition with faster charge transfer through Pd atoms in the core Pd2Ge lattice. In situ Raman spectroscopy depicts the emergence of a CoOOH phase on applying potential and shows that the phase is stable with increasing potential and time without getting converted to CoO2. Density functional theory calculations indicate that the Pd2Ge lattice induces distortion in the CoO phase and generates unpaired spins in a nonmagnetic CoOOH system resulting in an increase in the OER activity and durability. The existence of spin density even after electrocatalysis is verified from electron paramagnetic resonance spectroscopy. We have thus successfully synthesized intermetallic supported CoO during synthesis and rigorously verified the role played by an intermetallic Pd2Ge core in enhancing charge transfer, generating spin density, improving electrochemical durability, and imparting mechanical stability to a thin CoOOH overlayer. Differential electrochemical mass spectrometry has been explored to visualize the instantaneous generation of oxygen gas during the onset of the reaction.