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Record W4413915288 · doi:10.3390/catal15090827

Tuning High-Entropy Oxides for Oxygen Evolution Reaction Through Electrocatalytic Water Splitting: Effects of (MnFeNiCoX)3O4 (X = Cr, Cu, Zn, and Cd) on Electrocatalytic Performance

2025· article· en· W4413915288 on OpenAlexaff
Milad Zehtab Salmasi, Amir Narimani, Ali Omidkar, Hua Song

Bibliographic record

VenueCatalysts · 2025
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOxygen evolutionWater splittingMaterials scienceChemical engineeringInorganic chemistryChemistryElectrochemistryPhysical chemistryCatalysisElectrode

Abstract

fetched live from OpenAlex

This research presents the development of spinel-type high-entropy oxide (HEO) catalysts with the general composition (MnFeNiCoX)3O4, where X represents Cr, Cu, Zn, and Cd, synthesized through a solution combustion method. The impact of the fifth metal element on the oxygen evolution reaction (OER) was systematically explored using structural, morphological, and electrochemical characterization techniques. Among the various compositions, the Cr-containing catalyst, (MnFeNiCoCr)3O4, displayed outstanding electrocatalytic behavior, delivering a notably low overpotential of 323 mV at a current density of 10 mA/cm2 in 1.0 M KOH—surpassing the performance of benchmark RuO2. Additionally, this material exhibited the smallest Tafel slope (56 mV/dec), the greatest double-layer capacitance (3.35 mF/cm2), and the most extensive electrochemically active surface area, all indicating enhanced charge transfer capability and high catalytic proficiency. The findings highlight the potential of element tailoring in HEOs as a promising strategy for optimizing water oxidation catalysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.218
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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