Spin-State Transition Represses the Surface Reconstruction for Efficient Water Oxidation
Bibliographic record
Abstract
Spin-state manipulation holds great potential for navigating the reconstructed behavior during the oxygen evolution reaction (OER) due to the modified d–p orbital hybridization. Herein, we proposed a spin-state transition repressed surface reconstruction strategy by fabricating the iridium (Ir)-doped NiCo hydroxide with low spin-state Co sites as an efficient and stable OER catalyst. Ir doping induces spin asymmetry to be broken, alters the Co spin state from e g 2 to e g 0.6, and changes the orbital hybridization of Co(Ni)-O, effectively reducing the oxidation of metal sites in the catalytic reaction due to the alleviated accumulation of OH – on the surface, thus repressing the surface reconstruction. Notably, the stable Ir-NiCo LDH exhibited a low overpotential (245 mV at 10 mA cm –2 ) and steadily operated for over 100 h. The enhanced performance resulted from the introduction of high-active Ir and the up-shifted Co d-band center, which promote the generation of key *OOH intermediates. This work supplies a new platform for the development of effective OER catalysts based on the relationship between the electronic spin-state and surface reconstruction.
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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".