Bimetallic active site nuclear‐shell heterostructure enables efficient dual‐functional electrocatalysis in alkaline media
Bibliographic record
Abstract
Abstract Hydrogen, as a green and clean next‐generation fuel, is a key to achieving the goal of carbon neutrality. Constructing an electrocatalyst with bifunctional hydrogen evolution and oxygen evolution activity in the same electrolyte is a key technology for producing hydrogen via water splitting. Herein, a bimetallic active site catalyst, which possessed an edge‐riched MoS 2 nanoflakes array vertically growing on cubic CoS 2 , forming a nuclear‐shell heterogeneous configuration, termed CSC‐MoS 2 @CoS 2 . was reported The optimal CSC‐MoS 2 @CoS 2 ‐24 possessed good dual‐functional electrocatalytic activity (hydrogen evolution (HER), 10 mA·cm −2 @241.5 mV and oxygen evolution (OER), 10 mA·cm −2 @350 mV). Especially, CSC‐MoS 2 @CoS 2 ‐24 exhibited an extremely high mass activity for HER, and only required an overpotential of ~ 550 mV when reaching a large current density of 1422 mA·mg −1 , which was 20.6‐fold that of the bulk CoS 2 (69 mA·mg −1 ), as well as exhibiting stability of up to 100 h. The good electrocatalytic performance was attributed to the nuclear‐shell heterostructure of MoS 2 @CoS 2 hybrid could bring critical synergies, improving efficient mass transfer and electron transfer processes between CoS 2 and MoS 2 , which collaboratively promoted the electrocatalytic kinetics. It is foreseeable that the method proposed in this work will have guiding value for the preparation of dual‐functional electrocatalysts with multi‐interface heterostructures by assembling layered sulfides on cubic sulfides.
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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".