Designing Nanoporous Non-noble High Entropy Alloys as Efficient Catalysts for the Hydrogen Evolution Reaction DOI
Lixin Chen, Zhiwen Chen, Xue Yao

et al.

Energy & Fuels, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

Hydrogen represents a promising clean energy; however, the application of hydrogen energy is limited by prohibitively expensive commercial Pt/C catalyst for evolution reaction (HER). In this work, we designed non-noble high entropy alloy (HEA) catalysts FeCoNiCuMo with diversified active centers, which have an excellent catalytic performance HER. Density functional theory calculations indicate that Fe, Co, and Ni sites strong adsorption H* could facilitate water splitting, while Cu Mo weak promote formation H2. As proof concept, synthesized nanoporous (NP) ball milling dealloying to further increase resulting in onset potential 0 V vs reversible electrode (RHE) overpotential 68 mV at −10 mA cm–2, are even comparable catalyst. Our work highlights great NP HEA HER accelerates industrial energy.

Language: Английский

High entropy alloy electrocatalysts DOI

Guoliang Gao,

Yangyang Yu, Guang Zhu

et al.

Journal of Energy Chemistry, Journal Year: 2024, Volume and Issue: 99, P. 335 - 364

Published: Aug. 3, 2024

Language: Английский

Citations

13
Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion DOI Creative Commons
Tianyi Wang, Qilong Wu, Yun Han

et al.

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 6, 2025

Electrochemical reactions are pivotal for energy conversion and storage to achieve a carbon-neutral sustainable society, optimal electrocatalysts essential their industrial applications. Theoretical modeling methodologies, such as density functional theory (DFT) molecular dynamics (MD), efficiently assess electrochemical reaction mechanisms electrocatalyst performance at atomic levels. However, its intrinsic algorithm limitations high computational costs large-scale systems generate gaps between experimental observations calculation simulation, restricting the accuracy efficiency of design. Combining machine learning (ML) is promising strategy accelerate development electrocatalysts. The ML-DFT frameworks establish accurate property–structure–performance relations predict verify novel electrocatalysts' properties performance, providing deep understanding mechanisms. ML-based methods also solution MD DFT. Moreover, integrating ML experiment characterization techniques represents cutting-edge approach insights into structural, electronic, chemical changes under working conditions. This review will summarize DFT current application status design in various conversions. underlying physical fundaments, advancements, challenges be summarized. Finally, future research directions prospects proposed guide revolution.

Language: Английский

Citations

1
A review of high-entropy materials with their unique applications DOI Creative Commons

Juanna Ren,

Vilas Y. Kumkale,

Hua Hou

et al.

Advanced Composites and Hybrid Materials, Journal Year: 2025, Volume and Issue: 8(2)

Published: March 3, 2025

High-entropy materials (HEMs) constitute an innovative category of advanced distinguished by their distinctive atomic arrangements and remarkable multifunctional attributes. This thorough overview critically analyzes the core principles, synthesis methods, novel applications HEMs, emphasizing transformative potentials in electromagnetic biological fields. study examines how high configurational entropy effect, lattice distortion, slow diffusion mechanisms facilitate stabilization single-phase systems including numerous primary elements. Recent advancements HEM development have demonstrated exceptional skills wave absorption, attaining reflection losses up to - 35.10 dB via nano-domain designs synergistic dielectric-magnetic loss mechanisms. Including rare-earth elements has substantially affected magnetic ordering transition temperatures, with La-based compounds displaying spontaneous magnetization approximately 15.2 emu/g. In biomedical applications, formulations attained improved biocompatibility a diminished Young's modulus (69-140 GPa) corrosion resistance. review provides detailed roadmap for researchers engineers focused on practical application materials, through methodical analysis current developments energy storage, catalysis, shielding, applications. We emphasize significance composition design processing parameters customized features specific technological while recognizing key difficulties future research avenues this swiftly advancing sector.

Language: Английский

Citations

1
Structural Self-Regulation-Promoted NO Electroreduction on Single Atoms DOI
Xue Yao,

Linke Huang,

Ethan Halpren

et al.

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(48), P. 26249 - 26256

Published: Nov. 20, 2023

Simultaneously elevating loading and activity of single atoms (SAs) is desirable for SA-containing catalysts, including single-atom catalysts (SACs). However, the fast self-nucleation SAs limits loading, confined by adsorption-energy scaling relationships on monotonous SAs. Here, we theoretically design a novel type catalyst generated two-step structural self-regulation. In thermodynamic self-regulation step, divacancies in graphene spontaneously pull up from transition metal supports (dv-g/TM; TM = fcc Co, hcp Ni, Cu), leading to expectably high The subsequent kinetic step involving an adsorbate-assisted reversible vacancy migration dynamically alters coordination environments SAs, helping circumvent relationships, consequently, as-designed dv-g/Ni can catalyze NO-to-NH3 conversion at low limiting potential -0.25 V vs RHE.

Language: Английский

Citations

22
Adapting OC20-Trained EquiformerV2 Models for High-Entropy Materials DOI
Christian M. Clausen, Jan Rossmeisl, Zachary W. Ulissi

et al.

The Journal of Physical Chemistry C, Journal Year: 2024, Volume and Issue: 128(27), P. 11190 - 11195

Published: July 2, 2024

Computational high-throughput studies, especially in research on high-entropy materials and catalysts, are hampered by high-dimensional composition spaces myriad structural microstates. They present bottlenecks to the conventional use of density functional theory calculations, consequently, machine-learned potentials is becoming increasingly prevalent atomic structure simulations. In this communication, we show results adjusting fine-tuning pretrained EquiformerV2 model from Open Catalyst Project infer adsorption energies *OH *O out-of-domain alloy Ag–Ir–Pd–Pt–Ru. By applying an energy filter based local environment binding site, zero-shot inference markedly improved, through few-shot yields state-of-the-art accuracy. It also found that EquiformerV2, assuming role general machine learning potential, able inform a smaller, more focused direct model. This knowledge distillation setup boosts performance complex sites. Collectively, shows foundational learned ordered intermetallic structures can be extrapolated highly disordered solid-solutions. With vastly accelerated computational throughput these models, hitherto infeasible material space now readily accessible.

Language: Английский

Citations

8
PtIrFeCoNiMo high-entropy alloy nanodendrites for boosting the alkaline hydrogen oxidation performance DOI
Xiaolong Ma, Shuang Zhang,

Yaojiang Zhou

et al.

Journal of Materials Chemistry A, Journal Year: 2024, Volume and Issue: 12(15), P. 8862 - 8868

Published: Jan. 1, 2024

PtIrFeCoNiMo HEA NDs show excellent alkaline HOR performance due to optimizing the HBE and OHBE by synergistic high-entropy effect.

Language: Английский

Citations

7
Atomic Design of High-Entropy Alloys for Electrocatalysis DOI
Junlin Liu, Yile Zhang, Yiran Ding

et al.

ACS Materials Letters, Journal Year: 2024, Volume and Issue: 6(7), P. 2642 - 2659

Published: May 30, 2024

High-entropy alloys (HEAs) contain five or more main elements, and each element ranges in content from 5% to 35%. Due the abundant selectivity of excellent structural stability, adjustable active centers, HEAs have been widely used electrocatalysis. Designing HEA catalysts at atomic scale can deeply describe their complexity accurately reflect relationship between structure catalytic performance. In this Review, design HEA-based electrocatalysts is introduced it evaluated terms activity, selectivity, efficiency. Ingenuity level customize composition geometric HEAs, thereby enhancing intrinsic activity site, creating new sites, improving operational stability. The Review provides insights into electrocatalytic properties guidance for synthesis advanced viewpoint fabrication.

Language: Английский

Citations

7
High entropy materials: potential catalysts for electrochemical water Splitting DOI
Zhong Wang,

Xinjia Tan,

Ziyu Ye

et al.

Green Chemistry, Journal Year: 2024, Volume and Issue: 26(18), P. 9569 - 9598

Published: Jan. 1, 2024

A comprehensive overview of the use HEM as a catalyst for HER, OER, and water splitting was provided.

Language: Английский

Citations

6
High-Entropy Alloys in Catalysis: Progress, Challenges, and Prospects DOI Creative Commons
Liang Sun, Kaihua Wen, Guanjie Li

et al.

ACS Materials Au, Journal Year: 2024, Volume and Issue: 4(6), P. 547 - 556

Published: Sept. 29, 2024

High-entropy alloys (HEAs) have become pivotal materials in the field of catalysis, offering unique advantages due to their diverse elemental compositions and complex atomic structures. Recent advances computational techniques, particularly density functional theory (DFT) machine learning (ML), significantly enhanced our understanding design HEAs for use catalysis. These innovative atomistic simulations shed light on properties HEAs, enabling discovery optimization catalysis solid-solution This Perspective discusses recent studies that illustrate progress It offers an overview properties, constraints, prospects emphasizing roles enhance catalytic activity selectivity. The discussion underscores capabilities as multifunctional catalysts with stable presented insights aim inspire future experimental efforts address challenges fine-tuning improved performance.

Language: Английский

Citations

6
Multi-site intermetallic Ni3Mo effectively boosts selective ammonia synthesis DOI
Hong Zhou, Yanbin Qu, Yanchen Fan

et al.

Applied Catalysis B Environment and Energy, Journal Year: 2023, Volume and Issue: 339, P. 123133 - 123133

Published: July 29, 2023

Language: Английский

Citations

14