Engineering <i>d–p</i> Orbital Hybridization in Mo‐O Species of Medium‐Entropy Metal Oxides as Highly Active and Stable Electrocatalysts toward Ampere‐Level Water/Seawater Splitting
Bibliographic record
Abstract
Abstract Optimizing the electronic structure of electrocatalysts is of particular importance to enhance the intrinsic activity of active sites in water/seawater. Herein, a series of medium‐entropy metal oxides of X(NiMo)O2/NF (X = Mn, Fe, Co, Cu and Zn) is designed via a rapid carbothermal shocking method. Among them, the optimized medium‐entropy metal oxide (FeNiMo)O2/NF delivered remarkable HER performance, where the overpotentials as low as 110 and 141 mV are realized at 1000 mA cm−2 (@60 °C) in water and seawater. Meanwhile, medium‐entropy metal oxide (FeNiMo)O2/NF only required overpotentials of as low as 330 and 380 mV to drive 1000 mA cm−2 for OER in water and seawater (@60 °C). Theoretical calculations showed that the multiple‐metal synergistic effect in medium‐entropy metal oxides can effectively enhance the d–p orbital hybridization of Mo─O bond, reduce the energy barrier of H* adsorbed at the Mo sites. Meanwhile, Fe sites in medium‐entropy metal oxide can act as the real OER active center, resulting in a good bifunctional activity. In all, this work provides a feasible strategy for the development of highly active and stable medium‐entropy metal oxide electrocatalysts for ampere‐level water/seawater splitting.
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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".