In Situ Active Site for CO Activation in Fe-Catalyzed Fischer-Tropsch Synthesis from Machine Learning

Inhibiting Overoxidation of Dynamically Evolved RuO2 to Achieve a Win-Win in Activity-Stability for Acidic Water Electrolysis

Proton exchange membrane (PEM) water electrolysis offers an efficient route to large-scale green hydrogen production, in which the RuO2 catalyst exhibits superior activity but limited stability. Unveiling the atomic-scale structural evolution during operando reaction conditions is critical but remains a grand challenge for enhancing the durability of the RuO2 catalyst in the acidic oxygen evolution reaction (a-OER). This study proposes an adaptive machine learning workflow to elucidate the potential-dependent state-to-state global evolution of the RuO2(110) surface within a complex composition and configuration space, revealing the correlation between structural patterns and stability. We identify the active state with distorted RuO5 units that self-evolve at low potential, which exhibits minor Ru dissolution and an activity self-promotion phenomenon. However, this state exhibits a low potential resistance capacity (PRC) and evolves into inert RuO4 units at elevated potential. To enhance PRC and mitigate the overevolution of the active state, we explore the metal doping engineering and uncover an inverse volcano-type doping rule: the doped metal-oxygen bond strength should significantly differ from the Ru-O bond. This rule provides a theoretical framework for designing stable RuO2-based catalysts and clarifies current discrepancies regarding the roles of different metals in stabilizing RuO2. Applying this rule, we predict and confirm experimentally that Na can effectively stabilize RuO2 in its active state. The synthesized Na-RuO2 operates in a-OER for over 1800 h without any degradation and enables long-term durability in PEM electrolysis. This work enhances our understanding of the operando structural evolution of RuO2 and aids in designing durable catalysts for a-OER.

Planar chlorination engineering induced symmetry-broken single-atom site catalyst for enhanced CO2 electroreduction

Breaking the geometric symmetry of traditional metal-N4 sites and further boosting catalytic activity are significant but challenging. Herein, planar chlorination engineering is proposed for successfully converting the traditional Zn-N4 site with low activity and selectivity for CO2 reduction reaction (CO2RR) into highly active Zn-N3 site with broken symmetry. The optimal catalyst Zn-SA/CNCl-1000 displays a highest faradaic efficiency for CO (FECO) around 97 ± 3% and good stability during 50 h test at high current density of 200 mA/cm2 in zero-gap membrane electrode assembly (MEA) electrolyzer, with promising application in industrial catalysis. At -0.93 V vs. RHE, the partial current density of CO (JCO) and the turnover frequency (TOF) value catalyzed by Zn-SA/CNCl-1000 are 271.7 ± 1.4 mA/cm2 and 29325 ± 151 h-1, as high as 29 times and 83 times those of Zn-SA/CN-1000 without planar chlorination engineering. The in-situ extended X-ray absorption fine structure (EXAFS) measurements and density functional theory (DFT) calculation reveal the adjacent C-Cl bond induces the self-reconstruction of Zn-N4 site into the highly active Zn-N3 sites with broken symmetry, strengthening the adsorption of *COOH intermediate, and thus remarkably improving CO2RR activity.

Structure-reactivity relationships in CO2 hydrogenation to C2+ chemicals on Fe-based catalysts

Catalytic conversion of carbon dioxide (CO2) to value-added products represents an important avenue towards achieving carbon neutrality. In this respect, iron (Fe)-based catalysts were recognized as the most promising for the production of C2+ chemicals via the CO2 hydrogenation reaction. However, the complex structural evolution of the Fe catalysts, especially during the reaction, presents significant challenges for establishing the structure-reactivity relationships. In this review, we provide critical analysis of recent in situ and operando studies on the transformation of Fe-based catalysts in the hydrogenation of CO2 to hydrocarbons and alcohols. In particular, the effects of composition, promoters, support, and particle size on reactivity; the role of the catalyst's activation procedure; and the catalyst's evolution under reaction conditions will be addressed.

Do Rh-Hydride Phases Contribute to the Catalytic Activity of Rh Catalysts under Reductive Conditions?

Rh-hydride phases were believed to be key causes of the exceptional catalytic ability of Rh catalysts under H2 reductive conditions. Here, we utilize the large-scale machine-learning-based global optimization to explore millions of Rh bulk, surface, and nanoparticle structures in contact with H2, which rules out the presence of subsurface/interstitial H in Rh and Rh-hydride phases as thermodynamically stable phases under ambient conditions. Instead, an exceptional Rh-H affinity is identified for surface Rh atoms in Rh nanoparticles that can accommodate a high concentration of adsorbed H, with the surface Rh to H ratio reaching ∼2.5, featuring stable six-H-coordinated Rh, [RhH6]. Such [RhH6] species forming at edged Rh sites are found to be the key intermediates in the electrochemical hydrogen evolution reaction (HER) on Rh. Guided by theory, our synthesized Rh concave nanocubes with a high density of edged Rh sites achieve a Tafel slope of 28.4 mV dec-1 and a low overpotential of 36.1 mV at jECSA = 1 mA cm-2, which outperforms commercial Pt/C and other morphologies of Rh catalysts. Our results clarify the active phase in Rh-H nanosystems and guide the catalyst design by precise morphology control of nanocatalysts.

Local-Softening Stochastic Surface Walking for Fast Exploration of Corrugated Potential Energy Surfaces

Global potential energy surface (PES) exploration provides a unique route to predict the thermodynamic and kinetic properties of unknown materials, but the task is highly challenging for systems with tight covalent bonds. Here, we develop the local-softening stochastic surface walking (LS-SSW) method for scanning corrugated PESs. LS-SSW transforms the vibrational mode space of a system by adding pairwise penalty potentials with a self-adaption mechanism, which helps to delocalize and soften the strong local modes. This allows the stochastic surface walking (SSW) method to capture more efficiently the correct local atomic movement toward nearby minima and simultaneously reduce the barrier height of reactions. As a result, the local trapping time in searching for the corrugated PES is greatly reduced. LS-SSW can be applied generally to the reaction pathway sampling and the global PES exploration of both clusters and crystals, the high efficiency of which is demonstrated in searching the reaction pathways between C4H6 isomers, finding the global minimum of carbon clusters up to 360 atoms, and constructing the global PES of Fe7C3 material.

Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity

Linear α-olefins (1-alkenes) are critical comonomers for ethene copolymerization. A major impediment in the development of new homogeneous Fe catalysts for ethene oligomerization to produce comonomers and other important commercial products is the prediction of propagation versus termination rates that control the α-olefin distribution (e.g., 1-butene through 1-decene), which is often referred to as a K-value. Because the transition states for propagation versus termination are generally separated by less than a one kcal mol-1 difference in energy, this selectivity cannot be accurately predicted by either DFT or wavefunction methods (even DLPNO-CCSD(T)). Therefore, we developed a sub-kcal mol-1 accuracy machine learning model based on several hundred experimental selectivity values and straightforward 2D chemical and physical features that enables the prediction of α-olefin distribution K-values. As part of our model, we developed a new ad hoc feature that boosted the model performance. This machine learning model captures the effects of a broad range of ligand architectures and chemically nonintuitive trends in oligomerization selectivity. Our machine learning model was experimentally validated by prediction of a K-value for a new Fe phosphaneyl-pyridinyl-quinoline catalyst followed by experimental measurement that showed precise agreement. In addition to quantitative predictions, we demonstrate how this machine learning model can provide qualitative catalyst design using proximity of pairs type analysis.

Redefining the Symphony of Light Aromatic Synthesis Beyond Fossil Fuels: A Journey Navigating through a Fe-Based/HZSM-5 Tandem Route for Syngas Conversion

The escalating concerns about traditional reliance on fossil fuels and environmental issues associated with their exploitation have spurred efforts to explore eco-friendly alternative processes. Since then, in an era where the imperative for renewable practices is paramount, the aromatic synthesis industry has embarked on a journey to diversify its feedstock portfolio, offering a transformative pathway toward carbon neutrality stewardship. This Review delves into the dynamic landscape of aromatic synthesis, elucidating the pivotal role of renewable resources through syngas/CO2 utilization in reshaping the industry's net-zero carbon narrative. Through a meticulous examination of recent advancements, the current Review navigates the trajectory toward admissible aromatics production, highlighting the emergence of Fischer-Tropsch tandem catalysis as a game-changing approach. Scrutinizing the meliorated interplay of Fe-based catalysts and HZSM-5 molecular sieves would uncover the revolutionary potential of rationale design and optimization of integrated catalytic systems in driving the conversion of syngas/CO2 into aromatic hydrocarbons (especially BTX). In essence, the current Review would illuminate the path toward cutting-edge research through in-depth analysis of the transformative power of tandem catalysis and its capacity to propel carbon neutrality goals by unraveling the complexities of renewable aromatic synthesis and paving the way for a carbon-neutral and resilient tomorrow.

In Situ Surfaced Mn-Mn Dimeric Sites Dictate CO Hydrogenation Activity and C2 Selectivity over MnRh Binary Catalysts

Massive ethanol production has long been a dream of human society. Despite extensive research in past decades, only a few systems have the potential of industrialization: specifically, Mn-promoted Rh (MnRh) binary heterogeneous catalysts were shown to achieve up to 60% C2 oxygenates selectivity in converting syngas (CO/H2) to ethanol. However, the active site of the binary system has remained poorly characterized. Here, large-scale machine-learning global optimization is utilized to identify the most stable Mn phases on Rh metal surfaces under reaction conditions by exploring millions of likely structures. We demonstrate that Mn prefers the subsurface sites of Rh metal surfaces and is able to emerge onto the surface forming MnRh surface alloy once the oxidative O/OH adsorbates are present. Our machine-learning-based transition state exploration further helps to resolve automatedly the whole reaction network, including 74 elementary reactions on various MnRh surface sites, and reveals that the Mn-Mn dimeric site at the monatomic step edge is the true active site for C2 oxygenate formation. The turnover frequency of the C2 product on the Mn-Mn dimeric site at MnRh steps is at least 107 higher than that on pure Rh steps from our microkinetic simulations, with the selectivity to the C2 product being 52% at 523 K. Our results demonstrate the key catalytic role of Mn-Mn dimeric sites in allowing C-O bond cleavage and facilitating the hydrogenation of O-terminating C2 intermediates, and rule out Rh metal by itself as the active site for CO hydrogenation to C2 oxygenates.

Artificial intelligence driven molecule adsorption prediction (AIMAP) applied to chirality recognition of amino acid adsorption on metals

Predicting the adsorption structure of molecules has long been a challenging topic given the coupled complexity of surface binding sites and molecule flexibility. Here, we develop AIMAP, an Artificial Intelligence Driven Molecule Adsorption Prediction tool, to achieve the general-purpose end-to-end prediction of molecule adsorption structures. AIMAP features efficient exploration of the global potential energy surface of the adsorption system based on global neural network (G-NN) potential, by rapidly screening qualified adsorption patterns and fine searching using stochastic surface walking (SSW) global optimization. We demonstrate the AIMAP efficiency in constructing the Cu-HCNO6 adsorption database, encompassing 1 182 351 distinct adsorption configurations of 9592 molecules on three copper surfaces. AIMAP is then utilized to identify the best adsorption structure for 18 amino acids (AAs) on achiral Cu surfaces and the chiral Cu(3,1,17) S surface. We find that AAs chemisorb on copper surfaces in their highest deprotonated state, through both the carboxylate-amino skeleton and side groups. The chiral recognition is identified for the d-preference of Asp, Glu, and Tyr, and l-preference for His. The physical origin for the enantiospecific adsorption is thus rationalized, pointing to the critical role of the competitive adsorption between functional side groups and the carboxylate-amino skeleton at surface low-coordination sites.

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

The distinctive characteristics of alloy catalysts, encompassing composition, structure, and modifiable adsorption sites, present significant potential for the development of highly efficient electrocatalysts for oxygen evolution/reduction reactions [oxygen evolution reactions (OERs)/oxygen reduction reactions (ORRs)]. Machine learning (ML) methods can quickly establish the relationship between material features and catalytic activity, thus accelerating the development of alloy electrocatalysts. However, the current abundance of features presents a crucial challenge in selecting the most pertinent ones. In this study, we explored seven intrinsic features directly derived from the material's structure, with a specific focus on the chemical environment of active sites and their nearest neighbors. An accurate and efficient ML model to predict potential bifunctional oxygen electrocatalysts based on the intrinsic features of AB-type alloy active sites and intermediate free energies in the OERs/ORRs was established. These features possess clear physical and chemical meanings, closely linked to the electronic and geometric structures of active sites and neighboring atoms, thereby providing indispensable insights for the discovery of high-performance electrocatalysts. The ML model achieved R2 scores of 0.827, 0.913, and 0.711 for the predicted values of the three intermediate (OH, O, OOH) free energies, with corresponding mean absolute errors of 0.175, 0.242, and 0.200 eV, respectively. These results indicate that the ML model exhibits high accuracy in predicting the intermediate free energies. Furthermore, the ML model exhibited a prediction efficiency 150,000 times faster than traditional density functional theory calculations. This work will offer valuable insights and a framework for facilitating the rapid design of potential catalysts by ML methods.

Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments

Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions. A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions. Owing to the large number of possible Miller indices and terminations involved in multielement solids, extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of ab initio calculations. Here, we demonstrate, using the case of iron carbides in environments with varied carbon chemical potentials, that the stable surface composition and geometry of multielement solids under reactive conditions, which involve large compositional and conformational spaces, can be predicted at ab initio accuracy using an approach that combines the bond valence model, Gaussian process regression, and ab initio thermodynamics. Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis.

Explainable machine-learning predictions for catalysts in CO2-assisted propane oxidative dehydrogenation

Propylene is an important raw material in the chemical industry that needs new routes for its production to meet the demand. The CO2-assisted oxidative dehydrogenation of propane (CO2-ODHP) represents an ideal way to produce propylene and uses the greenhouse gas CO2. The design of catalysts with high efficiency is crucial in CO2-ODHP research. Data-driven machine learning is currently of great interest and gaining popularity in the heterogeneous catalysis field for guiding catalyst development. In this study, the reaction results of CO2-ODHP reported in the literature are combined and analyzed with varied machine learning algorithms such as artificial neural network (ANN), k-nearest neighbors (KNN), support vector regression (SVR) and random forest regression (RF)and were used to predict the propylene space-time yield. Specifically, the RF method serves as a superior performing algorithm for propane conversion and propylene selectivity prediction, and SHapley Additive exPlanations (SHAP) based on the Shapley value performs fine model interpretation. Reaction conditions and chemical components show different impacts on catalytic performance. The work provides a valuable perspective for the machine learning in light alkane conversion, and helps us to design catalyst by catalytic performance hidden in the data of literatures.

Precise solid-phase synthesis of CoFe@FeOx nanoparticles for efficient polysulfide regulation in lithium/sodium-sulfur batteries

Complex metal nanoparticles distributed uniformly on supports demonstrate distinctive physicochemical properties and thus attract a wide attention for applications. The commonly used wet chemistry methods display limitations to achieve the nanoparticle structure design and uniform dispersion simultaneously. Solid-phase synthesis serves as an interesting strategy which can achieve the fabrication of complex metal nanoparticles on supports. Herein, the solid-phase synthesis strategy is developed to precisely synthesize uniformly distributed CoFe@FeOx core@shell nanoparticles. Fe atoms are preferentially exsolved from CoFe alloy bulk to the surface and then be carburized into a FexC shell under thermal syngas atmosphere, subsequently the formed FexC shell is passivated by air, obtaining CoFe@FeOx with a CoFe alloy core and a FeOx shell. This strategy is universal for the synthesis of MFe@FeOx (M = Co, Ni, Mn). The CoFe@FeOx exhibits bifunctional effect on regulating polysulfides as the separator coating layer for Li-S and Na-S batteries. This method could be developed into solid-phase synthetic systems to construct well distributed complex metal nanoparticles.

Topology-Determined Structural Genes Enable Data-Driven Discovery and Intelligent Design of Potential Metal Oxides for Inert C-H Bond Activation

The identification of appropriate structural genes that influence the active-site configuration for a given reaction is critical for discovering potential catalysts with reduced reaction barriers. In this study, we introduce bulk-phase topology-derived tetrahedral descriptors as a means of expressing a catalyst's "material structural genes". We combine this approach with an interpretable machine learning model to accurately and efficiently predict the effective barrier associated with methane C-H bond cleavage across a wide range of metal oxides (MOs). These structural genes enable high-throughput catalyst screening for low-temperature methane activation and ultimately identify 13 candidate catalysts from a pool of 9095 MOs that are recommended for experimental synthesis. The topology-based method that we describe can also be extended to facilitate high-throughput catalyst screening and design for other dehydrogenation reactions.

An optimal Fe-C coordination ensemble for hydrocarbon chain growth: a full Fischer-Tropsch synthesis mechanism from machine learning

Fischer-Tropsch synthesis (FTS, CO + H2 → long-chain hydrocarbons) because of its great significance in industry has attracted huge attention since its discovery. For Fe-based catalysts, after decades of efforts, even the product distribution remains poorly understood due to the lack of information on the active site and the chain growth mechanism. Herein powered by a newly developed machine-learning-based transition state (ML-TS) exploration method to treat properly reaction-induced surface reconstruction, we are able to resolve where and how long-chain hydrocarbons grow on complex in situ-formed Fe-carbide (FeCx) surfaces from thousands of pathway candidates. Microkinetics simulations based on first-principles kinetics data further determine the rate-determining and the selectivity-controlling steps, and reveal the fine details of the product distribution in obeying and deviating from the Anderson-Schulz-Flory law. By showing that all FeCx phases can grow coherently upon each other, we demonstrate that the FTS active site, namely the A-P5 site present on reconstructed Fe3C(031), Fe5C2(510), Fe5C2(021), and Fe7C3(071) terrace surfaces, is not necessarily connected to any particular FeCx phase, rationalizing long-standing structure-activity puzzles. The optimal Fe-C coordination ensemble of the A-P5 site exhibits both Fe-carbide (Fe4C square) and metal Fe (Fe3 trimer) features.

Highly Reduced Ruthenium Carbide Carbonyl Clusters: Synthesis, Molecular Structure, Reactivity, Electrochemistry, and Computational Investigation of [Ru6C(CO)15]4

The reaction of [Ru6C(CO)16]2- (1) with NaOH in DMSO resulted in the formation of a highly reduced [Ru6C(CO)15]4- (2), which was readily protonated by acids, such as HBF4·Et2O, to [HRu6C(CO)15]3- (3). Oxidation of 2 with [Cp2Fe][PF6] or [C7H7][BF4] in CH3CN resulted in [Ru6C(CO)15(CH3CN)]2- (5), which was quantitatively converted into 1 after exposure to CO atmosphere. The reaction of 2 with a mild methylating agent such as CH3,I afforded the purported [Ru6C(CO)14(COCH3)]3- (6). By employing a stronger reagent, that is, CF3SO3CH3, a mixture of [HRu6C(CO)16]- (4), [H3Ru6C(CO)15]- (7), and [Ru6C(CO)15(CH3CNCH3)]- (8) was obtained. The molecular structures of 2-5, 7, and 8 were determined by single-crystal X-ray diffraction as their [NEt4]4[2]·CH3CN, [NEt4]3[3], [NEt4][4], [NEt4]2[5], [NEt4][7], and [NEt4][8]·solv salts. The carbyne-carbide cluster 6 was partially characterized by IR spectroscopy and ESI-MS, and its structure was computationally predicted using DFT methods. The redox behavior of 2 and 3 was investigated by electrochemical and IR spectroelectrochemical methods. Computational studies were performed in order to unravel structural and thermodynamic aspects of these octahedral Ru-carbide carbonyl clusters displaying miscellaneous ligands and charges in comparison with related iron derivatives.

CO2 Hydrogenation to Gasoline and Aromatics: Mechanistic and Predictive Insights from DFT, DRIFTS and Machine Learning

The emission of CO2 from fossil fuels is the largest driver of global climate change. To realize the target of a carbon-neutrality by 2050, CO2 capture and utilization is crucial. The efficient conversion of CO2 to C5+ gasoline and aromatics remains elusive mainly due to CO2 thermodynamic stability and the high energy barrier of the C-C coupling step. Herein, advances in mechanistic understanding via Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), density functional theory (DFT), and microkinetic modeling are discussed. It further emphasizes the power of machine learning (ML) to accelerate the search for optimal catalysts. A significant effort has been invested into this field of research with volumes of experimental and characterization data, this study discusses how they can be used as input features for machine learning prediction in a bid to better understand catalytic properties capable of accelerating breakthroughs in the process.

Machine Learning Descriptors for Data-Driven Catalysis Study

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

In situ catalytic cells for x-ray absorption spectroscopy measurement

In catalysis, determining the relationship between the dynamic electronic and atomic structure of the catalysts and the catalytic performance under actual reaction conditions is essential to gain a deeper understanding of the reaction mechanism since the structure evolution induced by the absorption of reactants and intermediates affects the reaction activity. Hard x-ray spectroscopy methods are considered powerful and indispensable tools for the accurate identification of local structural changes, for which the development of suitable in situ reaction cells is required. However, the rational design and development of spectroscopic cells is challenging because a balance between real rigorous reaction conditions and a good signal-to-noise ratio must be reached. Here, we summarize the in situ cells currently used in the monitoring of thermocatalysis, photocatalysis, and electrocatalysis processes, focusing especially on the cells utilized in the BL14W1-x-ray absorption fine structure beamline at the Shanghai Synchrotron Radiation Facility, and highlight recent endeavors on the acquisition of improved spectra under real reaction conditions. This review provides a full overview of the design of in situ cells, aiming to guide the further development of portable and promising cells. Finally, perspectives and crucial factors regarding in situ cells under industrial operating conditions are proposed.

Positive Valent Metal Sites in Electrochemical CO2 Reduction Reaction

The discovery of high-performance catalysts for the electrochemical CO₂ reduction reaction (CO₂ RR) has faced an enormous challenge for years. The lack of cognition about the surface active structures or centers of catalysts in complex conditions limits the development of advanced catalysts for CO₂ RR. Recently, the positive valent metal sites (PVMS) are demonstrated as a kind of potential active sites, which can facilitate carbon dioxide (CO₂ ) activation and conversation but are always unstable under reduction potentials. Many advanced technologies in theory and experiment have been utilized to understand and develop excellent catalysts with PVMS for CO₂ RR. Here, we present an introduction of some typical catalysts with PVMS in CO₂ RR and give some understanding of the activity and stability for these related catalysts.

Facile CO bond cleavage on polynuclear vanadium nitride clusters V4N5. Phys Chem Chem Phys 2022;24:29765-29771. [PMID: 36458914 DOI: 10.1039/d2cp04304a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Identifying the structural configurations of precursors for CO dissociation is fundamentally interesting and industrially important in the fields of, e.g., Fischer-Tropsch synthesis. Herein, we demonstrated that CO could be dissociated on polynuclear vanadium nitride V₄N₅^- clusters at room temperature, and a key intermediate, with CO in a N-assisted tilted bridge coordination where the C-O bond ruptures easily, was discovered. The reaction was characterized by mass spectrometry, photoelectron spectroscopy, and quantum-chemistry calculations, and the nature of the adsorbed CO on product V₄N₅CO^- was further characterized by a collision-induced dissociation experiment. Theoretical analysis evidences that CO dissociation is predominantly governed by the low-coordinated V and N atoms on the (V₃N₄)VN^- cluster and the V₃N₄ moiety resembles a support. This finding strongly suggests that a novel mode for facile CO dissociation was identified in a gas-phase cluster study.

The spatially separated active sites for holes and electrons boost the radicals generation for toluene degradation

Hydroxyl (⸱OH) and superoxide (⸱O₂^-) radicals are the main drivers for photocatalysis in toluene degradation, but their generation mechanisms are still ambiguous due to the lack of direct evidence. The spatially separated active sites for holes and electrons can help to clarify the dynamic process of radicals generation. By performing theoretical calculations, it is demonstrated that the spatially separated active sites for holes and electrons on the Bi₂O₂CO₃ surface can be constructed by introducing oxygen vacancies in the [Bi₂O₂]²⁺ layer. H₂O and O₂ molecules can be better adsorbed and activated at hole and electron active sites, separately. Accordingly, the pristine and defective Bi₂O₂CO₃ are prepared. The dynamic behavior of H₂O and O₂ molecules at the matching active sites is revealed, which indicates the efficient adsorption of reactants and the substantial production of radicals. Significantly, the specificity of the spatially separated holes and electrons active sites for ⸱OH and ⸱O₂^- radicals generation, respectively, is demonstrated by in situ EPR with the H₂O vapor atmosphere. This work provides a design concept for unraveling reaction mechanisms to realize controllable radicals generation.

Light olefin synthesis from a diversity of renewable and fossil feedstocks: state-of the-art and outlook

Light olefins are important feedstocks and platform molecules for the chemical industry. Their synthesis has been a research priority in both academia and industry. There are many different approaches to the synthesis of these compounds, which differ by the choice of raw materials, catalysts and reaction conditions. The goals of this review are to highlight the most recent trends in light olefin synthesis and to perform a comparative analysis of different synthetic routes using several quantitative characteristics: selectivity, productivity, severity of operating conditions, stability, technological maturity and sustainability. Traditionally, on an industrial scale, the cracking of oil fractions has been used to produce light olefins. Methanol-to-olefins, alkane direct or oxidative dehydrogenation technologies have great potential in the short term and have already reached scientific and technological maturities. Major progress should be made in the field of methanol-mediated CO and CO₂ direct hydrogenation to light olefins. The electrocatalytic reduction of CO₂ to light olefins is a very attractive process in the long run due to the low reaction temperature and possible use of sustainable electricity. The application of modern concepts such as electricity-driven process intensification, looping, CO₂ management and nanoscale catalyst design should lead in the near future to more environmentally friendly, energy efficient and selective large-scale technologies for light olefin synthesis.

In Situ Active Site for Fe-Catalyzed Fischer-Tropsch Synthesis: Recent Progress and Future Challenges

Fischer-Tropsch synthesis (FTS) that converts syngas into long-chain hydrocarbons is a key technology in the chemical industry. As one of the best catalysts for FTS, the Fe-based composite develops rich solid phases (metal, oxides, and carbides) in the catalytic reaction, which triggered the quest for the true active site in catalysis in the past century. Recent years have seen great advances in probing the active-site structure using modern experimental and theoretical tools. This Perspective serves to highlight these latest achievements, focusing on the geometrical structure and thermodynamic stability of Fe carbide bulk phases, the exposed surfaces, and their relationship to FTS activity. The current reaction mechanisms on CO activation and carbon chain growth are also discussed, in the context of theoretical models and experimental evidence. We also present the outlook regarding the current challenges in Fe-based FTS.

Stable All-Solid-State Lithium Metal Batteries Enabled by Machine Learning Simulation Designed Halide Electrolytes

Solid electrolytes (SEs) with superionic conductivity and interfacial stability are highly desirable for stable all-solid-state Li-metal batteries (ASSLMBs). Here, we employ neural network potential to simulate materials composed of Li, Zr/Hf, and Cl using stochastic surface walking method and identify two potential unique layered halide SEs, named Li₂ZrCl₆ and Li₂HfCl₆, for stable ASSLMBs. The predicted halide SEs possess high Li⁺ conductivity and outstanding compatibility with Li metal anodes. We synthesize these SEs and demonstrate their superior stability against Li metal anodes with a record performance of 4000 h of steady lithium plating/stripping. We further fabricate the prototype stable ASSLMBs using these halide SEs without any interfacial modifications, showing small internal cathode/SE resistance (19.48 Ω cm²), high average Coulombic efficiency (∼99.48%), good rate capability (63 mAh g^-1 at 1.5 C), and unprecedented cycling stability (87% capacity retention for 70 cycles at 0.5 C).

Automated search for optimal surface phases (ASOPs) in grand canonical ensemble powered by machine learning

The surface of a material often undergoes dramatic structure evolution under a chemical environment, which, in turn, helps determine the different properties of the material. Here, we develop a general-purpose method for the automated search of optimal surface phases (ASOPs) in the grand canonical ensemble, which is facilitated by the stochastic surface walking (SSW) global optimization based on global neural network (G-NN) potential. The ASOP simulation starts by enumerating a series of composition grids, then utilizes SSW-NN to explore the configuration and composition spaces of surface phases, and relies on the Monte Carlo scheme to focus on energetically favorable compositions. The method is applied to silver surface oxide formation under the catalytic ethene epoxidation conditions. The known phases of surface oxides on Ag(111) are reproduced, and new phases on Ag(100) are revealed, which exhibit novel structure features that could be critical for understanding ethene epoxidation. Our results demonstrate that the ASOP method provides an automated and efficient way for probing complex surface structures that are beneficial for designing new functional materials under working conditions.