Charge Self‐Regulation of Metallic Heterostructure Ni<sub>2</sub>P@Co<sub>9</sub>S<sub>8</sub> for Alkaline Water Electrolysis with Ultralow Overpotential at Large Current Density
Bibliographic record
Abstract
Abstract Designing cost‐effective alkaline water‐splitting electrocatalysts is essential for large‐scale hydrogen production. However, nonprecious catalysts face challenges in achieving high activity and durability at a large current density. An effective strategy for designing high‐performance electrocatalysts is regulating the active electronic states near the Fermi‐level, which can improve the intrinsic activity and increase the number of active sites. As a proof‐of‐concept, it proposes a one‐step self‐assembly approach to fabricate a novel metallic heterostructure based on nickel phosphide and cobalt sulfide (Ni 2 P@Co 9 S 8 ) composite. The charge transfer between active Ni sites of Ni 2 P and Co─Co bonds of Co 9 S 8 efficiently enhances the active electronic states of Ni sites, and consequently, Ni 2 P@Co 9 S 8 exhibits remarkably low overpotentials of 188 and 253 mV to reach the current density of 100 mA cm −2 for the hydrogen evolution reaction and oxygen evolution reaction, respectively. This leads to the Ni 2 P@Co 9 S 8 incorporated water electrolyzer possessing an ultralow cell voltage of 1.66 V@100 mA cm −2 with ≈100% retention over 100 h, surpassing the commercial Pt/C║RuO 2 catalyst (1.9 V@100 mA cm −2 ). This work provides a promising methodology to boost the activity of overall water splitting with ultralow overpotentials at large current density by shedding light on the charge self‐regulation of metallic heterostructure.
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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".