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High Entropy Alloys for Highly Efficient Catalytic Applications

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Catalytic Materials".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 21

Special Issue Editors

Zhejiang Institute of Tianjin University, Shaoxing 312300, China
Interests: theoretical catalysis; first principles calculation; machine learning; electrocatalysis

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Guest Editor
INAMAT^2-Departamento de Ciencias, Edificio de los Acebos, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Spain
Interests: preparation, characterization and catalytic activity of metal-supported catalysts; surface properties of solids; pollutants adsorption; environmental management; industrial waste valorization
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Special Issue Information

Dear Colleagues,

High entropy alloys (HEAs), composed of five or more principal elements in near-equimolar ratios, have emerged as a groundbreaking class of materials due to their unique structural and electronic properties. Their high configurational entropy stabilizes disordered solid solutions, enabling exceptional thermal stability, mechanical durability, and tunable surface reactivity. These attributes position HEAs as promising candidates for catalytic applications, addressing critical challenges in energy conversion, environmental remediation, and sustainable chemical synthesis. HEAs offer a sustainable alternative by combining abundant elements and tailoring active sites through compositional diversity.

This collection aims to explore the design, synthesis, and mechanistic understanding of HEAs for catalysis. Key focus areas include the following:

  • Catalytic Performance: Applications in hydrogen (HER), oxygen evolution/oxygen reduction (OER/ORR), CO2 reduction, and hydrocarbon conversion.
  • Structure–Property Relationships: Linking atomic-scale configurations to catalytic activity and selectivity.
  • Scalability and Stability: Strategies for synthesizing HEAs via scalable methods and ensuring durability under harsh operational conditions.

Authors are encouraged to submit interdisciplinary studies integrating computational modeling, advanced characterization (e.g., in situ spectroscopy), and real-world performance evaluations. This initiative seeks to advance HEAs as next-generation catalysts, driving innovation in renewable energy and green chemistry.

Dr. Mao Peng
Prof. Dr. Antonio Gil Bravo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • high entropy alloys
  • electrocatalysis
  • reaction mechanism
  • catalyst design
  • molecular simulation
  • high throughput computing
  • machine learning
  • interdisciplinary studies

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