Layer-structured NiFe nanosheets on CoNi nanowires for enhanced oxygen evolution reaction
Bibliographic record
Abstract
Abstract Efficient electrocatalysts are critical for the oxygen evolution reaction (OER) that occurs during water electrolysis. Herein, a simple and low-cost strategy of assembling CoNi nanowire arrays with NiFe nanosheets on flexible carbon cloth (CC) support as an efficient OER catalyst is developed. This unique ‘nanosheets on nanowires’ structure design increases its specific surface area, enabling access to more active sites. The resulting NiFe@H-CoNi/CC catalyst exhibits excellent OER activity (280 mV overpotential at 100 mA cm −2 ) with a Tafel slope of 36 mV dec −1 and also has outstanding durability at high current operation conditions (over 100 h at 100 mA cm −2 ). Moreover, in-situ Raman analysis suggests that the NiOOH is the realistic OER active phase. This ‘nanosheet on nanowire’ design gives a means for fabricating OER catalysts that are both high-performance and long-lasting.
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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".