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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".