journal article·2026-03-13Open access

Bringing Order to Chaos in High-Entropy Electrocatalysts

Jing Yu, Ren He, Neus G. Bastús, Jordi Arbiol, Andreu Cabot

ACS Energy Letters· Volume 11· Issue 3

Abstract

High-entropy materials (HEMs) have emerged as a transformative platform for electrocatalysis. Their appeal lies in the vast compositional versatility enabled by the combination of five or more elements, which generates a rich diversity of atomic configurations and surface sites ideally suited for complex multistep reactions. Recent years have witnessed explosive growth in the development of HEMs across diverse material classes and their application to a wide range of electrochemical reactions. Yet significant challenges remain to fully harness their capabilities while managing their intrinsic structural and chemical complexity. Advancing the field requires exploring compositional space, pinpointing reaction sites, and achieving atomic-level control of surface composition and organization. Much remains to be done, calling for breakthroughs in materials design, characterization, and synthesis strategies and technologies. Ultimately, as highlighted here, beyond electrocatalytic applications, HEMs embody a new paradigm in materials discovery, linking precise engineering, correlative multimodal characterization, and high-throughput experimentation and computation.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Electrocatalysts for Energy Conversion · CO2 Reduction Techniques and Catalysts · Electrochemical Analysis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Catalonia Institute for Energy Research (IREC)

Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST

ICREA, Pg. Lluis Companys

Funding

European Commission · European Regional Development Fund · Agència de Gestió d'Ajuts Universitaris i de Recerca · NextGenerationEU · Generalitat de Catalunya

References (71)

Show source-provided references
1. 10.1021/acsnano.2c0809610.1021/acsnano.2c08096 ↗
2. 10.1016/j.cej.2025.16998710.1016/j.cej.2025.169987 ↗
3. 10.1021/acs.chemmater.3c0036310.1021/acs.chemmater.3c00363 ↗
4. 10.1016/j.ijhydene.2023.07.01110.1016/j.ijhydene.2023.07.011 ↗
5. 10.1038/s44160-025-00854-z10.1038/s44160-025-00854-z ↗
6. 10.1016/j.ijhydene.2025.02.18910.1016/j.ijhydene.2025.02.189 ↗
7. 10.1039/D4EE01912A10.1039/D4EE01912A ↗
8. 10.1021/acsami.2c1162710.1021/acsami.2c11627 ↗
9. 10.1021/acs.chemmater.5c0001810.1021/acs.chemmater.5c00018 ↗
10. 10.1021/acsnano.3c1073110.1021/acsnano.3c10731 ↗
11. 10.1021/acs.jpcc.2c0508310.1021/acs.jpcc.2c05083 ↗
12. 10.1038/s41893-023-01101-z10.1038/s41893-023-01101-z ↗
13. 10.1002/anie.20240501710.1002/anie.202405017 ↗
14. 10.1016/j.ijrmhm.2021.10550410.1016/j.ijrmhm.2021.105504 ↗
15. 10.1016/j.joule.2024.04.01210.1016/j.joule.2024.04.012 ↗
16. 10.1038/s41467-024-45309-910.1038/s41467-024-45309-9 ↗
17. 10.1016/j.jechem.2024.07.04910.1016/j.jechem.2024.07.049 ↗
18. 10.1038/s41578-024-00654-510.1038/s41578-024-00654-5 ↗
19. 10.1002/adma.20251263310.1002/adma.202512633 ↗
20. 10.3390/e1609474910.3390/e16094749 ↗
21. 10.1038/s41467-024-52349-810.1038/s41467-024-52349-8 ↗
22. 10.1038/s41467-025-56421-910.1038/s41467-025-56421-9 ↗
23. 10.1103/PhysRevMaterials.5.10360410.1103/PhysRevMaterials.5.103604 ↗
24. 10.1038/s41578-019-0121-410.1038/s41578-019-0121-4 ↗
25. 10.1038/s41467-025-58648-y10.1038/s41467-025-58648-y ↗
26. 10.1103/lsjr-wcr110.1103/lsjr-wcr1 ↗
27. 10.1016/S0038-1098(00)00301-X10.1016/S0038-1098(00)00301-X ↗
28. 10.1103/RevModPhys.57.28710.1103/RevModPhys.57.287 ↗
29. 10.1103/PhysRevLett.44.128810.1103/PhysRevLett.44.1288 ↗
30. 10.1021/acs.chemmater.5c0259810.1021/acs.chemmater.5c02598 ↗
31. 10.1088/0022-3719/8/4/00310.1088/0022-3719/8/4/003 ↗
32. 10.1063/10.003434310.1063/10.0034343 ↗
33. 10.1021/acs.jpclett.5c0025910.1021/acs.jpclett.5c00259 ↗
34. 10.1021/acs.jpclett.4c0124010.1021/acs.jpclett.4c01240 ↗
35. 10.1002/adma.20250418010.1002/adma.202504180 ↗
36. 10.1039/D5TA01956G10.1039/D5TA01956G ↗
37. 10.1021/acsami.5c0691710.1021/acsami.5c06917 ↗
38. 10.1016/j.mcat.2022.11214110.1016/j.mcat.2022.112141 ↗
39. 10.1002/anie.20251845810.1002/anie.202518458 ↗
40. 10.1021/acs.iecr.5c0222710.1021/acs.iecr.5c02227 ↗
41. 10.1039/D4EE02817A10.1039/D4EE02817A ↗
42. 10.1021/jacs.4c1597910.1021/jacs.4c15979 ↗
43. 10.1002/adma.20230371910.1002/adma.202303719 ↗
44. 10.1016/j.ensm.2023.03.02210.1016/j.ensm.2023.03.022 ↗
45. 10.1002/adfm.20251385910.1002/adfm.202513859 ↗
46. 10.1002/adma.20241233710.1002/adma.202412337 ↗
47. 10.1021/acscatal.4c0279210.1021/acscatal.4c02792 ↗
48. 10.1021/acs.nanolett.3c0183110.1021/acs.nanolett.3c01831 ↗
49. 10.1039/D2NH00377E10.1039/D2NH00377E ↗
50. 10.1002/aisy.20240098610.1002/aisy.202400986 ↗
51. Pinto-Huguet, I.; Botifoll, M.; Chen, X.; Eriksen, M. B.; Yu, J.; Isella, G.; Cabot, A.; Merino, G.; Arbiol, J. Enhancing Atomic-Resolution in Electron Microscopy: A Frequency-Domain Deep Learning Denoiser. Adv. Intell. Syst. 202610.1002/aisy.202501077.10.1002/aisy.202501077 ↗
52. 10.1002/adma.202506785.10.1002/adma.202506785. ↗
53. 10.1038/s41570-024-00575-510.1038/s41570-024-00575-5 ↗
54. 10.1126/science.adw775110.1126/science.adw7751 ↗
55. 10.1021/jacs.5c1374110.1021/jacs.5c13741 ↗
56. 10.1021/acsnano.4c1498810.1021/acsnano.4c14988 ↗
57. 10.1021/acsenergylett.4c0270310.1021/acsenergylett.4c02703 ↗
58. 10.1080/21663831.2023.222439710.1080/21663831.2023.2224397 ↗
59. 10.1038/s41467-025-56421-910.1038/s41467-025-56421-9 ↗
60. 10.1016/j.patter.2022.10055310.1016/j.patter.2022.100553 ↗
61. 10.1016/j.joule.2023.06.00310.1016/j.joule.2023.06.003 ↗
62. 10.1016/j.isci.2025.11357710.1016/j.isci.2025.113577 ↗
63. 10.1021/jacs.5c0411710.1021/jacs.5c04117 ↗
64. 10.1002/ange.20240711610.1002/ange.202407116 ↗
65. 10.1002/adma.20251042410.1002/adma.202510424 ↗
66. 10.1002/adma.20242046510.1002/adma.202420465 ↗
67. 10.1021/jacs.3c1448610.1021/jacs.3c14486 ↗
68. 10.1021/acsmaterialsau.5c0013510.1021/acsmaterialsau.5c00135 ↗
69. Ren, Z.; Zhang, Z.; Tian, Y.; Li, J. Accelerated High-Entropy Alloys Discovery for Electrocatalysis via Robotic-Aided Active Learning. Poster presented at the NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World, 2023.
70. 10.1039/C9EE01338E10.1039/C9EE01338E ↗
71. 10.1039/D4SU00310A10.1039/D4SU00310A ↗