Evidence map›Paper›PMID 39015415›Full record

ArticleThe journal of physical chemistry. C, Nanomaterials and interfaces2024

Unraveling Reactivity Origin of Oxygen Reduction at High-Entropy Alloy Electrocatalysts with a Computational and Data-Driven Approach.

Yang Huang, Shih-Han Wang, Xiangrui Wang, Noushin Omidvar, Luke E K Achenie, Sara E Skrabalak, Hongliang Xin

Abstract read
In one paragraph

Article in The journal of physical chemistry. C, Nanomaterials and interfaces, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Elemental Stability in Mixed Noble and Non-Noble Metal High Entropy Alloy Nanoparticle Electrocatalysts.Chemistry of materials : a publication of the American Chemical Society · 2026
    Article
  3. Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction.Chemistry of materials : a publication of the American Chemical Society · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yang HuangDepartment of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.
Shih-Han WangDepartment of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.ORCID https://orcid.org/0000-0003-4418-2080
Xiangrui WangDepartment of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.
Noushin OmidvarDepartment of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.ORCID https://orcid.org/0000-0001-6766-8548
Luke E K AchenieDepartment of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.ORCID https://orcid.org/0000-0001-9850-5346
Sara E SkrabalakDepartment of Chemistry, Indiana University - Bloomington, Bloomington, Indiana 47405, United States.ORCID https://orcid.org/0000-0002-1873-100X
Hongliang XinDepartment of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.ORCID https://orcid.org/0000-0001-9344-1697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-entropy alloys (HEAs), characterized as compositionally complex solid solutions with five or more metal elements, have emerged as a novel class of catalytic materials with unique attributes. Because of the remarkable diversity of multielement sites or site ensembles stabilized by configurational entropy, human exploration of the multidimensional design space of HEAs presents a formidable challenge, necessitating an efficient, computational and data-driven strategy over traditional trial-and-error experimentation or physics-based modeling. Leveraging deep learning interatomic potentials for large-scale molecular simulations and pretrained machine learning models of surface reactivity, our approach effectively rationalizes the enhanced activity of a previously synthesized PdCuPtNiCo HEA nanoparticle system for electrochemical oxygen reduction, as corroborated by experimental observations. We contend that this framework deepens our fundamental understanding of the surface reactivity of high-entropy materials and fosters the accelerated development and synthesis of monodisperse HEA nanoparticles as a versatile material platform for catalyzing sustainable chemical and energy transformations.

Identifiers

PMID39015415
PMCPMC11247481

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.