Field‐Controlled Hydroxide Dynamics Drive High‐Valence Surface Reconstruction of Ferromagnetic Alloy Nanocones Toward Efficient Oxygen Evolution
Bibliographic record
Abstract
Abstract Transition metal catalysts undergo dynamic surface reconstruction during the oxygen evolution reaction (OER), yet kinetic barriers inherently restrict the formation of high‐valent active species. Here, nanocone‐structured CoFeNi catalysts are designed to synergistically couple electric and magnetic field effects, tailoring interfacial microenvironments for efficient surface reconstruction. The nanocone tips generate intense localized electric fields that concentrate OH − ions, while their curvature amplifies magnetic flux density to extend the OH − distribution. In situ Raman and ATR‐FTIR spectroscopic analyses confirm that this near‐field coupling enriches interfacial OH − , accelerating metal hydroxylation and deprotonation by optimizing proton‐coupled electron transfer (PCET) kinetics. This synergy drives rapid reconstruction of the catalyst surface, promoting the formation of high‐valent Co 4+ species and activating the lattice oxygen mechanism essential for efficient OER. Electrochemical performance confirms that this high‐activity reconstruction leads to a 400% increase in current density at 1.57 V versus RHE under half‐cell conditions, along with a stable operation at 500 mA cm −2 (1.93 V) for over 500 h in a water electrolyzer. By correlating OH − dynamics with PCET‐mediated Co 4+ activation, this study demonstrates morphology‐engineered field manipulation as an effective approach to drive catalyst 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".