Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(8), P. 4855 - 4933

Published: March 27, 2023

Heterogeneous bimetallic catalysts have broad applications in industrial processes, but achieving a fundamental understanding on the nature of active sites at atomic and molecular level is very challenging due to structural complexity catalysts. Comparing features catalytic performances different entities will favor formation unified structure-reactivity relationships heterogeneous thereby facilitate upgrading current In this review, we discuss geometric electronic structures three representative types (bimetallic binuclear sites, nanoclusters, nanoparticles) then summarize synthesis methodologies characterization techniques for entities, with emphasis recent progress made past decade. The supported nanoparticles series important reactions are discussed. Finally, future research directions catalysis based and, more generally, prospective developments both practical applications.

Language: Английский

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Optimization strategies of high-entropy alloys for electrocatalytic applications

Chemical Science, Journal Year: 2023, Volume and Issue: 14(45), P. 12850 - 12868

Published: Jan. 1, 2023

This review summarizes the synthesis methods, characterization research progress and regulation strategies of HAEs in field electrocatalytic HER, HOR, OER, ORR, CO 2 RR, NRR AOR, providing deep understanding for future applications.

Language: Английский

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Advances and challenges in the electrochemical reduction of carbon dioxide

Chemical Science, Journal Year: 2024, Volume and Issue: 15(21), P. 7870 - 7907

Published: Jan. 1, 2024

This review highlights the structure–activity relationship of ECO 2 RR, provides a detailed summary advanced materials by analyzing electrocatalytic applications and reaction mechanisms, discusses challenges in both devices.

Language: Английский

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Designing strategies and enhancing mechanism for multicomponent high-entropy catalysts

Chemical Science, Journal Year: 2023, Volume and Issue: 14(4), P. 771 - 790

Published: Jan. 1, 2023

High-entropy materials (HEMs) are new-fashioned functional in the field of catalysis owing to their large designing space, tunable electronic structure, interesting "cocktail effect", and entropy stabilization effect. Many effective strategies have been developed design advanced catalysts for various important reactions. Herein, we firstly review so far optimizing HEM-based underlying mechanism revealed by both theoretical simulations experimental aspects. In light this overview, subsequently present some perspectives about development provide serviceable guidelines and/or inspiration further studying multicomponent catalysts.

Language: Английский

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Scalable Synthesis of Multi‐Metal Electrocatalyst Powders and Electrodes and their Application for Oxygen Evolution and Water Splitting

Angewandte Chemie International Edition, Journal Year: 2023, Volume and Issue: 62(12)

Published: Jan. 14, 2023

Multi-metal electrocatalysts provide nearly unlimited catalytic possibilities arising from synergistic element interactions. We propose a polymer/metal precursor spraying technique that can easily be adapted to produce large variety of compositional different multi-metal catalyst materials. To demonstrate this, 11 catalysts were synthesized, characterized, and investigated for the oxygen evolution reaction (OER). Further investigation most active OER catalyst, namely CoNiFeMoCr, revealed polycrystalline structure, operando Raman measurements indicate multiple sites are participating in reaction. Moreover, Ni foam-supported CoNiFeMoCr electrodes developed applied water splitting flow-through electrolysis cells with electrolyte gaps zero-gap membrane electrode assembly (MEA) configurations. The proposed alkaline MEA-type electrolyzers reached up 3 A cm-2 , 24 h demonstrated no loss current density 1 .

Language: Английский

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Discovering High Entropy Alloy Electrocatalysts in Vast Composition Spaces with Multiobjective Optimization

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(11), P. 7698 - 7707

Published: March 11, 2024

High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as their unique active site distributions break the scaling relations that limit activity conventional transition metal catalysts. Existing Bayesian optimization (BO)-based virtual screening approaches focus on catalytic sole objective and correspondingly tend to identify unlikely be entropically stabilized. Here, we overcome this limitation with multiobjective BO framework HEAs simultaneously targets activity, cost-effectiveness, entropic stabilization. With diversity-guided batch selection further boosting its data efficiency, readily identifies numerous candidates oxygen reduction reaction strike balance between all three objectives in hitherto unchartered HEA design spaces comprising up 10 elements.

Language: Английский

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Screening of Cu-Mn-Ni-Zn high-entropy alloy catalysts for CO2 reduction reaction by machine-learning-accelerated density functional theory

Applied Surface Science, Journal Year: 2024, Volume and Issue: 652, P. 159297 - 159297

Published: Jan. 7, 2024

Language: Английский

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Machine-Learning-Driven High-Entropy Alloy Catalyst Discovery to Circumvent the Scaling Relation for CO₂ Reduction Reaction

ACS Catalysis, Journal Year: 2022, Volume and Issue: 12(24), P. 14864 - 14871

Published: Nov. 22, 2022

To achieve an equitable energy transition toward net-zero 2050 goals, the electrochemical reduction of CO2 (CO2RR) to chemical feedstocks through utilizing both and renewable is particularly attractive. However, catalytic activity CO2RR limited by scaling relation adsorption energies intermediates. Circumventing a potential strategy breakthrough in activity. Herein, based on density functional theory (DFT) calculations, we designed high-entropy alloy (HEA) system FeCoNiCuMo with high for CO2RR. Machine learning models were developed considering 1280 sites predict COOH*, CO*, CHO*. The between CHO* circumvented rotation COOH* HEA surface, resulting outstanding limiting 0.29–0.51 V. This work not only accelerates development catalysts but also provides effective circumvent relation.

Language: Английский

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Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength

npj Computational Materials, Journal Year: 2022, Volume and Issue: 8(1)

Published: Nov. 12, 2022

Abstract Refractory high-entropy alloys (RHEAs) show significant elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. Exploring the vast RHEA compositional space experimentally is challenging, a small fraction of this has been explored date. This work demonstrates development state-of-the-art machine learning framework coupled with optimization methods intelligently explore drive search direction that improves high-temperature strengths. Our strength model shown significantly improved predictive accuracy relative approach, also provides inherent uncertainty quantification through repeated k -fold cross-validation. Upon developing validating robust prediction model, used discover RHEAs superior high temperature strength. We compositions can be customized maximum at specific temperature.

Language: Английский

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Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Advanced Materials, Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 9, 2023

Abstract Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, complex structure HEC poses challenges traditional experimental computational approaches, necessitating adoption machine learning. Microscopically, can model Hamiltonian system, enabling atomic‐level property investigations, while macroscopically, it analyze macroscopic material characteristics such hardness, melting point, ductility. Various algorithms, both methods deep neural networks, be employed research. Comprehensive accurate data collection, feature engineering, training selection through cross‐validation are crucial for establishing excellent ML models. also holds promise analyzing phase structures stability, constructing potentials simulations, facilitating design functional materials. Although some domains, magnetic device materials, still require further exploration, learning's potential is substantial. Consequently, become an indispensable understanding exploiting capabilities HEC, serving foundation new paradigm Artificial‐intelligence‐assisted exploration.

Language: Английский

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