Enhancing Co–O Covalency by Low Electronegativity Anion-Induced Charge Rebalance in a (Co,Fe)S<sub>2</sub>/Co<sub>3</sub>O<sub>4</sub> Composite for Efficient Oxygen Evolution Reaction
Bibliographic record
Abstract
The metal–oxygen covalency is the most advanced descriptor to understanding the relationship between the electronic structure and oxygen evolution reaction (OER) properties of oxide catalysts; however, its regulation strategy by an anion is very limited. Herein, we demonstrated that the Co–O covalency can be enlarged by introducing a second phase of disulfide (Co,Fe)S 2 coupled on the spinel Co 3 O 4 . The S and O with different electronegativity coordinated simultaneously with transition metal ions at the composite interface of (Co,Fe)S 2 /Co 3 O 4, which brings the oxygen charge shifting toward the metal ions. The strengthened covalency and the charge rebalance between oxygen and metal ions are observed directly by X-ray photoelectron spectroscopy and X-ray absorption spectroscopy, which are further confirmed by the calculated O 2p band center upshift to the Fermi level based on the density functional theory. The reinforced Co–O covalency resulted in an OER overpotential reduction of more than 100 mV compared to the pristine one. This work provides a rational way to enhance the metal–oxygen covalency by constructing a composite phase for elevating the intrinsic OER activity of the catalyst, which may be extendable to other oxides.
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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) | 0.001 | 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".