Revealing the structural evolution of CuAg composites during electrochemical carbon monoxide reduction.

Comprehending the catalyst structural evolution during the electrocatalytic process is crucial for establishing robust structure/performance correlations for future catalysts design. Herein, we interrogate the structural evolution of a promising Cu-Ag oxide catalyst precursor during electrochemical carbon monoxide reduction. By using extensive in situ and ex situ characterization techniques, we reveal that the homogenous oxide precursors undergo a transformation to a bimetallic composite consisting of small Ag nanoparticles enveloped by thin layers of amorphous Cu. We believe that the amorphous Cu layer with undercoordinated nature is responsible for the enhanced catalytic performance of the current catalyst composite. By tuning the Cu/Ag ratio in the oxide precursor, we find that increasing the Ag concentration greatly promotes liquid products formation while suppressing the byproduct hydrogen. CO2/CO co-feeding electrolysis and isotopic labelling experiments suggest that high CO concentrations in the feed favor the formation of multi-carbon products. Overall, we anticipate the insights obtained for Cu-Ag bimetallic systems for CO electroreduction in this study may guide future catalyst design with improved performance.

Electronic structure modification of metal phthalocyanines by a carbon nanotube support for efficient oxygen reduction to hydrogen peroxide.

An active and selective two-electron oxygen reduction reaction (2e- ORR) is required for efficient electrosynthesis of H2O2. This reaction can be promoted by metal phthalocyanines (MPcs), which serve as model catalysts with well-defined structures. MPc molecules have mostly been evaluated on conductive carbon-based substrates, including glassy carbon (GC) and carbon nanotubes (CNTs), yet their influence on the electrocatalytic properties is not well understood. This study demonstrated that the ORR activity per surface area was improved by up to 4-fold with MPc molecules supported on CNTs (MPc/CNTs, M = Co, Mn, and Fe) compared to MPc loaded directly on GC. Ultraviolet photoelectron spectroscopy and density functional theory calculations revealed that the CNTs modified the electronic structure of the MPc molecules to optimize the *OOH binding energy and boost the heterogeneous electron transfer rates. Detailed kinetic analysis enabled multiple reaction pathways to be decoupled to extract the metal-dependent intrinsic 2e-/4e- ORR activities. Finally, MPc/CNT catalysts were employed in an H2O2 electrosynthesis flow cell, which delivered an industrial-scale current density of -200 mA cm-2 and an H2O2 faradaic efficiency of 88.7 ± 0.6% with the CoPc/CNT catalyst in a neutral electrolyte.

Validating ΔΔG Selectivity Descriptor for Electrosynthesis of H2O2 from Oxygen Reduction Reaction.

Understanding selectivity trends is a crucial hurdle in the developing innovative catalysts for generating hydrogen peroxide through the two-electron oxygen reduction reaction (2e-ORR). The identification of selectivity patterns has been made more accessible through the introduction of a newly developed selectivity descriptor derived from thermodynamics, denoted as ΔΔG introduced in Chem Catal. 2023, 3(3), 100568. To validate the suitability of this parameter as a descriptor for 2e-ORR selectivity, we utilize an extensive library of 155 binary alloys. We validate that ΔΔG reliably depicts the selectivity trends in binary alloys reported for their high activity in the 2e-ORR. This analysis also enables the identification of nine selective 2e-ORR catalysts underscoring the efficacy of ΔΔG as 2e-ORR selectivity descriptor. This work highlights the significance of concurrently considering both selectivity and activity trends. This holistic approach is crucial for obtaining a comprehensive understanding in the identification of high-performance catalyst materials for optimal efficiency in various applications.

Vitamin C-induced CO2 capture enables high-rate ethylene production in CO2 electroreduction.

High-rate production of multicarbon chemicals via the electrochemical CO2 reduction can be achieved by efficient CO2 mass transport. A key challenge for C-C coupling in high-current-density CO2 reduction is how to promote *CO formation and dimerization. Here, we report molecularly enhanced CO2-to-*CO conversion and *CO dimerization for high-rate ethylene production. Nanoconfinement of ascorbic acid by graphene quantum dots enables immobilization and redox reversibility of ascorbic acid in heterogeneous electrocatalysts. Cu nanowire with ascorbic acid nanoconfined by graphene quantum dots (cAA-CuNW) demonstrates high-rate ethylene production with a Faradaic efficiency of 60.7% and a partial current density of 539 mA/cm2, a 2.9-fold improvement over that of pristine CuNW. Furthermore, under low CO2 ratio of 33%, cAA-CuNW still exhibits efficient ethylene production with a Faradaic efficiency of 41.8%. We find that cAA-CuNW increases *CO coverage and optimizes the *CO binding mode ensemble between atop and bridge for efficient C-C coupling. A mechanistic study reveals that ascorbic acid can facilitate *CO formation and dimerization by favorable electron and proton transfer with strong hydrogen bonding.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning.

The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods.

Accelerating the Search for New Solid Electrolytes: Exploring Vast Chemical Space with Machine Learning-Enabled Computational Calculations.

Discovering new solid electrolytes (SEs) is essential to achieving higher safety and better energy density for all-solid-state lithium batteries. In this work, we report machine learning (ML)-assisted high-throughput virtual screening (HTVS) results to identify new SE materials. This approach expands the chemical space to explore by substituting elements of prototype structures and accelerates an evaluation of properties by applying various ML models. The screening results in a few candidate materials, which are validated by density functional theory calculations and ab initio molecular dynamics simulations. The shortlisted oxysulfide materials satisfy key properties to be successful SEs. The advanced screening method presented in this work will accelerate the discovery of energy materials for related applications.

Insight into Defect Engineering of Atomically Dispersed Iron Electrocatalysts for High-Performance Proton Exchange Membrane Fuel Cell.

Atomically dispersed and nitrogen coordinated iron catalysts (Fe-NCs) demonstrate potential as alternatives to platinum-group metal (PGM) catalysts in oxygen reduction reaction (ORR). However, in the context of practical proton exchange membrane fuel cell (PEMFC) applications, the membrane electrode assembly (MEA) performances of Fe-NCs remain unsatisfactory. Herein, improved MEA performance is achieved by tuning the local environment of the Fe-NC catalysts through defect engineering. Zeolitic imidazolate framework (ZIF)-derived nitrogen-doped carbon with additional CO2 activation is employed to construct atomically dispersed iron sites with a controlled defect number. The Fe-NC species with the optimal number of defect sites exhibit excellent ORR performance with a high half-wave potential of 0.83 V in 0.5 M H2 SO4 . Variation in the number of defects allows for fine-tuning of the reaction intermediate binding energies by changing the contribution of the Fe d-orbitals, thereby optimizing the ORR activity. The MEA based on a defect-engineered Fe-NC catalyst is found to exhibit a remarkable peak power density of 1.1 W cm-2 in an H2 /O2 fuel cell, and 0.67 W cm-2  in an H2 /air fuel cell, rendering it one of the most active atomically dispersed catalyst materials at the MEA level.

Geometric Tuning of Single-Atom FeN4 Sites via Edge-Generation Enhances Multi-Enzymatic Properties.

Single-atom nanozymes (SAzymes) are considered promising alternatives to natural enzymes. The catalytic performance of SAzymes featuring homogeneous, well-defined active structures can be enhanced through elucidating structure-activity relationship and tailoring physicochemical properties. However, manipulating enzymatic properties through structural variation is an underdeveloped approach. Herein, the synthesis of edge-rich Fe single-atom nanozymes (FeNC-edge) via an H2 O2 -mediated edge generation is reported. By controlling the number of edge sites, the peroxidase (POD)- and oxidase (OXD)-like performance is significantly enhanced. The activity enhancement results from the presence of abundant edges, which provide new anchoring sites to mononuclear Fe. Experimental results combined with density functional theory (DFT) calculations reveal that FeN4 moieties in the edge sites display high electron density of Fe atoms and open N atoms. Finally, it is demonstrated that FeNC-edge nanozyme effectively inhibits tumor growth both in vitro and in vivo, suggesting that edge-tailoring is an efficient strategy for developing artificial enzymes as novel catalytic therapeutics.

Transition Metal Ion Doping on ZIF-8 Enhances the Electrochemical CO2 Reduction Reaction.

The electrochemical reduction of CO2  to diverse value-added chemicals is a unique, environmentally friendly approach for curbing greenhouse gas emissions while addressing sluggish catalytic activity and low Faradaic efficiency (FE) of electrocatalysts. Here, zeolite-imidazolate-frameworks-8 (ZIF-8) containing various transition metal ions-Ni, Fe, and Cu-at varying concentrations upon doping are fabricated for the electrocatalytic CO2 reduction reaction (CO2 RR) to carbon monoxide (CO) without further processing. Atom coordination environments and theoretical electrocatalytic performance are scrutinized via X-ray absorption spectroscopy (XAS) and density functional theory (DFT) calculations. Upon optimized Cu doping on ZIF-8, Cu0.5 Zn0.5 /ZIF-8 achieves a high partial current density of 11.57 mA cm-2 and maximum FE for CO of 88.5% at -1.0 V (versus RHE) with a stable catalytic activity over 6 h. Furthermore, the electron-rich sp2 C atom facilitates COOH* promotion after Cu doping of ZIF-8, leading to a local effect between the zinc-nitrogen (Zn-N4 ) and copper-nitrogen (Cu-N4 ) moieties. Additionally, the advanced CO2 RR pathway is illustrated from various perspectives, including the pre-H-covered state under the CO2 RR. The findings expand the pool of efficient metal-organic framework (MOF)-based CO2 RR catalysts, deeming them viable alternatives to conventional catalysts.

Catalytic activity trends of pyrite transition metal dichalcogenides for oxygen reduction and evolution.

Transition metal dichalcogenides (TMDs) have been considered as promising materials for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) electrocatalysis. While there have been numerous studies focusing on layered TMDs, the ORR and OER catalytic activity trends of various cubic pyrite TMDs have not been systematically explored yet. Herein, we investigated 12 earth abundant element-based pyrite TMDs (MX2, where M = Mn, Fe, Co, Ni and X = S, Se, Te) using density functional theory (DFT) calculations. We initially constructed surface Pourbaix diagrams to determine the most stable surface coverages under the reaction conditions and found that the oxidized surfaces are most energetically preferred in all cases. We then calculated the binding free energies of reaction intermediates (O*, OH* and OOH*) and established their scaling relations. The electrochemical ORR and OER performances were then displayed on two-dimensional volcano plots, which suggest MnS2, FeS2, NiTe2 and CoSe2 to be ORR active, and CoTe2 and CoSe2 to be OER active. In addition, we built multivariate linear regression models to predict ΔGO* and ΔGOH* using only atomic and bulk properties to readily estimate the catalytic activities of pyrite TMDs and to explore correlations between those properties. Particularly, we found that the electron affinity and bulk Bader charges of metal atoms are critical in determining the ORR and OER catalytic activities, which could be used as a guidance for future catalyst design.

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. Co0.5Mn0.5 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.

Electrochemical Hydrogen Peroxide Synthesis from Selective Oxygen Reduction over Metal Selenide Catalysts.

Se-based nanoalloys as an emerging class of metal chalcogenide with tunable crystalline structure, component distribution, and electronic structure have attracted considerable interest in renewable energy conversion and utilization. In this Letter, we report a series of nanosized M-Se catalysts (M = Cu, Ni, Co) as prepared from laser ablation method and screen their electrocatalytic performance for onsite H2O2 generation from selective oxygen reduction reaction (ORR) in alkaline media. A flexible control on 2e-/4e- ORR pathway has been achieved by engineering the alloying component. Moreover, through a feedback loop between theory and experiment an optimized scaling relationship between oxygenated ORR intermediates has been discovered on cubic Cu7.2Se4 nanocrystals, that is, the ensemble effect of isolated Cu component destabilizes O* binding while the ligand effect of Se to Cu fine-tunes the binding strength of OOH*, leading to a superb H2O2 selectivity above 90% over a wide potential window even after 1400 potential cycles.

Structural Insights into Multi-Metal Spinel Oxide Nanoparticles for Boosting Oxygen Reduction Electrocatalysis.

Multi-metal oxide (MMO) materials have significant potential to facilitate various demanding reactions by providing additional degrees of freedom in catalyst design. However, a fundamental understanding of the (electro)catalytic activity of MMOs is limited because of the intrinsic complexity of their multi-element nature. Additional complexities arise when MMO catalysts have crystalline structures with two different metal site occupancies, such as the spinel structure, which makes it more challenging to investigate the origin of the (electro)catalytic activity of MMOs. Here, uniform-sized multi-metal spinel oxide nanoparticles composed of Mn, Co, and Fe as model MMO electrocatalysts are synthesized and the contributions of each element to the structural flexibility of the spinel oxides are systematically studied, which boosts the electrocatalytic oxygen reduction reaction (ORR) activity. Detailed crystal and electronic structure characterizations combined with electrochemical and computational studies reveal that the incorporation of Co not only increases the preferential octahedral site occupancy, but also modifies the electronic state of the ORR-active Mn site to enhance the intrinsic ORR activity. As a result, nanoparticles of the optimized catalyst, Co0.25 Mn0.75 Fe2.0 -MMO, exhibit a half-wave potential of 0.904 V (versus RHE) and mass activity of 46.9 A goxide -1 (at 0.9 V versus RHE) with promising stability.

Atomic Structure-Free Representation of Active Motifs for Expedited Catalyst Discovery.

To discover new catalysts using density functional theory (DFT) calculations, binding energies of reaction intermediates are considered as descriptors to predict catalytic activities. Recently, machine learning methods have been developed to reduce the number of computationally intensive DFT calculations for a high-throughput screening. These methods require several steps such as bulk structure optimization, surface structure modeling, and active site identification, which could be time-consuming as the number of new candidate materials increases. To bypass these processes, in this work, we report an atomic structure-free representation of active motifs to predict binding energies. We identify binding site atoms and their nearest neighboring atoms positioned in the same layer and the sublayer, and their atomic properties are collected to construct fingerprints. Our method enabled a quicker training (200-400 s using CPU) compared to the previous deep-learning models and predicted CO and H binding energies with mean absolute errors (MAEs) of 0.120 and 0.105 eV, respectively. Our method is also capable of creating all possible active motifs without any DFT calculations and predicting their binding energies using the trained model. The predicted binding energy distributions can suggest promising candidates to accelerate catalyst discovery.

In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation.

Various databases of density functional theory (DFT) calculations for materials and adsorption properties are currently available. Using the Materials Project and GASpy databases of material stability and binding energies (H* and CO*), respectively, we evaluate multiple aspects of catalysts to discover active, stable, CO-tolerant, and cost-effective hydrogen evolution and oxidation catalysts. Finally, we suggest a few candidate materials for future experimental validations. We highlight that the stability analysis is easily obtainable but provides invaluable information to assess thermodynamic and electrochemical stability, bridging the gap between simulations and experiments. Furthermore, it reduces the number of expensive DFT calculations required to predict catalytic activities of surfaces by filtering out unstable materials.

Discovery of Acid-Stable Oxygen Evolution Catalysts: High-Throughput Computational Screening of Equimolar Bimetallic Oxides.

Discovering acid-stable, cost-effective, and active catalysts for oxygen evolution reaction (OER) is critical since this reaction is a bottleneck in many electrochemical energy conversion systems. The current systems use extremely expensive iridium oxide catalysts. Identifying Ir-free or less-Ir containing catalysts has been suggested as the goal, but no systematic strategy to discover such catalysts has been reported. In this work, we perform first-principles-based high-throughput catalyst screening to discover OER-active and acid-stable catalysts focusing on equimolar bimetallic oxides with space groups derived from those of IrOx. We develop an approach to evaluate acid-stability under the reaction condition by utilizing the Materials Project database and density functional theory (DFT) calculations. For acid-stable materials, we further investigate their OER catalytic activities and identify promising OER catalysts that satisfy all the desired properties: Co-Ir, Fe-Ir, and Mo-Ir bimetallic oxides. Based on the calculated results, we provide insights to efficiently perform future high-throughput screening to discover catalysts with desirable properties and discuss the remaining challenges.

A Porphyrinic Zirconium Metal-Organic Framework for Oxygen Reduction Reaction: Tailoring the Spacing between Active-Sites through Chain-Based Inorganic Building Units.

The oxygen reduction reaction (ORR) is central in carbon-neutral energy devices. While platinum group materials have shown high activities for ORR, their practical uses are hampered by concerns over deactivation, slow kinetics, exorbitant cost, and scarce nature reserve. The low cost yet high tunability of metal-organic frameworks (MOFs) provide a unique platform for tailoring their characteristic properties as new electrocatalysts. Herein, we report a new concept of design and present stable Zr-chain-based MOFs as efficient electrocatalysts for ORR. The strategy is based on using Zr-chains to promote high chemical and redox stability and, more importantly, tailor the immobilization and packing of redox active-sites at a density that is ideal to improve the reaction kinetics. The obtained new electrocatalyst, PCN-226, thereby shows high ORR activity. We further demonstrate PCN-226 as a promising electrode material for practical applications in rechargeable Zn-air batteries, with a high peak power density of 133 mW cm-2. Being one of the very few electrocatalytic MOFs for ORR, this work provides a new concept by designing chain-based structures to enrich the diversity of efficient electrocatalysts and MOFs.

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.

Toward Predicting Intermetallics Surface Properties with High-Throughput DFT and Convolutional Neural Networks.

The surface energy of inorganic crystals is important in understanding experimentally relevant surface properties and designing materials for many applications. Predictive methods and data sets exist for surface energies of monometallic crystals. However, predicting these properties for bimetallic or more complicated surfaces is an open challenge. Computing cleavage energy is the first step in calculating surface energy across a large space. Here, we present a workflow to predict cleavage energies ab initio using high-throughput DFT and a machine learning framework. We calculated the cleavage energy of 3033 intermetallic alloys with combinations of 36 elements and 47 space groups. This high-throughput workflow was used to seed a database of cleavage energies. The database was used to train a crystal graph convolutional neural network (CGCNN). The CGCNN model provides an accurate prediction of cleavage energy with a mean absolute test error of 0.0071 eV/Å2. It can also qualitatively reproduce nanoparticle surface distributions (Wulff constructions). Our workflow provides quantitative insights into unexplored chemical space by predicting which surfaces are relatively stable and therefore more realistic. The insights allow us to down-select interesting candidates that we can study with robust theoretical and experimental methods for applications such as catalyst screening and nanomaterials synthesis.