Tungsten‐Doped CoP Nanoneedle Arrays Grown on Carbon Cloth as Efficient Bifunctional Electrocatalysts for Overall Water Splitting
Bibliographic record
Abstract
Abstract The development of efficient and inexpensive bifunctional electrocatalysts with high activity and durability for both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are highly desirable but challenging. Herein, we report the design and preparation of tungsten‐doped cobalt phosphide nanoneedle arrays on conductive carbon cloth (W−CoP/CC) as highly efficient and stable HER and OER electrocatalysts for overall water splitting. The nanoneedle array provides enriched active sites and facilitates ion diffusion and the 3D conductive CC skeleton enables fast charge transport. Moreover, tungsten doping improves the water adsorption ability, optimizes the hydrogen adsorption energy, and increases the electrochemical active surface area thereby resulting in much improved HER and OER catalytic activity. The W−CoP/CC composite shows low overpotentials of 32 mV at a current density of 10 mA cm−2 in 0.5 M H2SO4 electrolyte for HER and 77 and 252 mV in 1 M KOH solution for HER and OER, respectively, outperforming most previously reported metal phosphide electrocatalysts. The electrolyzer for overall water splitting with W−CoP/CC as both the anode and cathode achieves a stable current density of 10 mA cm−2 at a low cell voltage of 1.59 V with no obvious voltage change even after operation for 20 h. The results provide a new strategy to design and prepare high‐performance non‐noble metal phosphides for overall water splitting.
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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".