Highly Active and Durable Single‐Atom Tungsten‐Doped NiS<sub>0.5</sub>Se<sub>0.5</sub> Nanosheet @ NiS<sub>0.5</sub>Se<sub>0.5</sub> Nanorod Heterostructures for Water Splitting
Bibliographic record
Abstract
Abstract Developing robust and highly active non‐precious electrocatalysts for the hydrogen/oxygen evolution reaction (HER/OER) is crucial for the industrialization of hydrogen energy. In this study, a highly active and durable single‐atom W‐doped NiS0.5Se0.5 nanosheet @ NiS0.5Se0.5 nanorod heterostructure (W‐NiS0.5Se0.5) electrocatalyst is prepared. W‐NiS0.5Se0.5 exhibits excellent catalytic activity for the HER and OER with ultralow overpotentials (39 and 106 mV for the HER and 171 and 239 mV for the OER at 10 and 100 mA cm−2, respectively) and excellent long‐term durability (500 h), outperforming commercial precious‐metal catalysts and many other previously reported transition‐metal‐based compounds (TMCs). The introduction of single‐atom W delocalizes the spin state of Ni, which results in an increase in the Ni d‐electron density. This causes the optimization of the adsorption/desorption process of H and a significant reduction in the adsorption free energy of the rate‐determining step (O* → OOH*), thus accelerating the thermodynamics and kinetics of the HER and OER. This work provides a rational feasible strategy to design single‐atom catalysts for water splitting and to develop advanced TMC electrocatalysts by regulating delocalized spin states.
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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".