What is the Real Origin of the Activity of Fe–N–C Electrocatalysts in the O₂ Reduction Reaction? Critical Roles of Coordinating Pyrrolic N and Axially Adsorbing Species

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(39), P. 18144 - 18152

Published: Sept. 22, 2022

Fe–N–C electrocatalysts have emerged as promising substitutes for Pt-based catalysts the oxygen reduction reaction (ORR). However, their real catalytic active site is still under debate. The underlying roles of different types coordinating N including pyridinic and pyrrolic in performance require thorough clarification. In addition, how to understand pH-dependent activity another urgent issue. Herein, we comprehensively studied 13 N-coordinated FeNxC configurations corresponding ORR through simulations which mimic realistic electrocatalytic environment on basis constant-potential implicit solvent models. We demonstrate that contributes a higher than N, FeN4C exhibits highest acidic media. Meanwhile, situ transformation *O-FeN4C *OH-FeN4C clarifies origin alkaline These findings can provide indispensable guidelines rational design better durable catalysts.

Language: Английский

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Electrochemical reduction of carbon dioxide to multicarbon (C₂₊) products: challenges and perspectives

Energy & Environmental Science, Journal Year: 2023, Volume and Issue: 16(11), P. 4714 - 4758

Published: Jan. 1, 2023

This review analyzes advanced catalysts and C 2+ synthesis mechanisms based on theoretical explorations in situ / operando characterizations. Triphasic interface optimization is discussed for the potential of industry-compatible stability.

Language: Английский

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High-entropy alloys in electrocatalysis: from fundamentals to applications

Chemical Society Reviews, Journal Year: 2023, Volume and Issue: 52(23), P. 8319 - 8373

Published: Jan. 1, 2023

In this review, we provide a comprehensive summary of recent advances in the synthesis strategies, design principles, and characterization technologies high entropy alloys, their applications various electrocatalytic conversion reactions.

Language: Английский

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Machine Learning: A New Paradigm in Computational Electrocatalysis

The Journal of Physical Chemistry Letters, Journal Year: 2022, Volume and Issue: 13(34), P. 7920 - 7930

Published: Aug. 18, 2022

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, uncovering scientific insights lie the center of development electrocatalysis. Despite certain success in experiments computations, it is still difficult to achieve above objectives due complexity systems vastness chemical space for candidate electrocatalysts. With advantage machine learning (ML) increasing interest electrocatalysis energy conversion storage, data-driven research motivated by artificial intelligence (AI) has provided new opportunities discover promising investigate dynamic reaction processes, extract knowledge from huge data. In this Perspective, we summarize recent applications ML electrocatalysis, including electrocatalysts simulation processes. Furthermore, interpretable methods are discussed accelerate generation. Finally, blueprint envisaged future

Language: Английский

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Perspectives on Advancing Sustainable CO₂ Conversion Processes: Trinomial Technology, Environment, and Economy

ACS Sustainable Chemistry & Engineering, Journal Year: 2024, Volume and Issue: 12(14), P. 5357 - 5382

Published: March 22, 2024

CO2 can be converted into value-added products such as fuels, chemicals, and building materials, adding an economic incentive for capture green economy, while also reducing the environmental footprint of hard-to-abate industries aviation, construction, metallurgy. Nonetheless, most available technologies direct conversion, promising, are still in early development stages, facing technical challenges their scale-up, questioning viability to truly instill a timely impact on global emissions. Furthermore, clear benefit should obtained new processes versus traditional ones they replacing market. In this perspective, we examine range conversion using thermal, electrical, photochemical routes mineralization including advancements role synthesizing resulting from methanol, methane, carbon monoxide, solid carbonates. We offer insights trends current research required direction expedite technological readiness attractive terms catalytic material reactor design. highlight important modeling (molecular process levels) enabling tool deploy these at commercial scale originating understanding behavior molecular level. Lastly, significance carrying out reliable life cycle analysis identifying hotspots well gaps technology that allows improving attractiveness.

Language: Английский

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Cu-based materials for electrocatalytic CO2 to alcohols: Reaction mechanism, catalyst categories, and regulation strategies

Journal of Energy Chemistry, Journal Year: 2024, Volume and Issue: 97, P. 593 - 611

Published: June 17, 2024

Language: Английский

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Electrochemical CO2 Reduction: Advances, Insights, Challenges, and Future Directions

Materials Today Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 101089 - 101089

Published: Feb. 1, 2025

Language: Английский

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A Universal Machine Learning Framework for Electrocatalyst Innovation: A Case Study of Discovering Alloys for Hydrogen Evolution Reaction

Advanced Functional Materials, Journal Year: 2022, Volume and Issue: 32(47)

Published: Sept. 16, 2022

Abstract Massive efforts have been made to develop efficient electrocatalysts for green hydrogen production. The introduction of machine learning (ML) has brought new opportunities the design electrocatalysts. However, current ML studies shown that efficiency and accuracy this method in electrocatalyst development are severely hindered by two major problems, high computational cost paid electronic or geometrical structures with accuracy, large errors resulted from those easily accessible relatively simple physical chemical properties lower level accuracy. Here, a universal framework is proposed achieves local structure optimization using potential (MLP) efficiently obtain accurate descriptors, combining graph convolutional neural networks, 43 high‐performance alloys successfully screened as evolution reaction 2973 candidates. More importantly, part best candidates identified verified experiments, one them (AgPd) systematically investigated ab initio calculations under realistic electrocatalytic environments further demonstrate significantly, can be compromised MLP optimized structural descriptor input, paradigm could established designing

Language: Английский

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Unraveling the improved CO2 adsorption and COOH* formation over Cu-decorated ZnO nanosheets for CO2 reduction toward CO

Chemical Engineering Journal, Journal Year: 2022, Volume and Issue: 452, P. 139701 - 139701

Published: Oct. 10, 2022

Language: Английский

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Selective and Stable CO₂ Electroreduction to CH₄ via Electronic Metal–Support Interaction upon Decomposition/Redeposition of MOF

Small, Journal Year: 2023, Volume and Issue: 19(41)

Published: June 10, 2023

The CO2 electroreduction to fuels is a feasible approach provide renewable energy sources. Therefore, it necessary conduct experimental and theoretical investigations on various catalyst design strategies, such as electronic metal-support interaction, improve the catalytic selectivity. Here solvent-free synthesis method reported prepare copper (Cu)-based metal-organic framework (MOF) precursor. Upon electrochemical reduction in aqueous electrolyte, undergoes situ decomposition/redeposition processes form abundant interfaces between Cu nanoparticles amorphous carbon supports. This Cu/C favors selective stable production of CH4 with Faradaic efficiency ≈55% at -1.4 V versus reversible hydrogen electrode (RHE) for 12.5 h. density functional theory calculation reveals crucial role interfacial sites support stabilizing key intermediates . adsorption COOH* CHO* interface up 0.86 eV stronger than that Cu(111), thus promoting formation envisioned strategy regulating interaction can selectivity stability toward specific product upon reduction.

Language: Английский

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