In Situ Electrochemical Oxyanion Steering of Water Oxidation Electrocatalysts for Optimized Activity and Stability
Bibliographic record
Abstract
Abstract Compared to traditional modulation by metal cations doping, oxyanions offer a higher possibility of mediating the performance of electrocatalysts toward oxygen evolution reaction (OER) due to their special polyanion configurations and large electronegativity. However, the mechanism and rules of oxyanions mediation remain poorly understood. Herein, an in situ electrochemical oxyanion (NO 3 − , PO 4 3− , SO 4 2− , or SeO 4 2− ) steering strategy to study the variation and rules of OER performance for transition‐metal (TM = Ni, Fe, Co) hydroxide electrocatalysts is reported. Electrocatalytic experiments indicate both activity and stability of oxyanion‐modified TM hydroxides follow the order of PO 4 3− > NO 3 − > SO 4 2− > SeO 4 2− . Electrochemical incorporation of PO 4 3− or NO 3 − improves activity and stability of TM hydroxides. Conversely, SO 4 2− or SeO 4 2− doping significantly accelerates TM leaching and thus impairs OER performance. Theoretical calculations reveal that electrochemical oxyanion doping simultaneously modulates TM‐O covalency and TM‐3 d band centers, correlating with TM stability and OER activity of TM hydroxides. This research constructs an oxyanion‐mediated rule for designing high‐performance electrocatalysts toward energy transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".