Strategic Atomic Layer Deposition and Electrospinning of Cobalt Sulfide/Nitride Composite as Efficient Bifunctional Electrocatalysts for Overall Water Splitting
Bibliographic record
Abstract
Abstract Reported herein is comprehensive study of a highly active and stable cobalt catalyst for overall water splitting. This composite SFCNF/Co1−xS@CoN, consisting of S‐doped flexible carbon nanofiber (SFCNF) matrix, Co1−xS nanoparticles, and CoN coatings, is prepared by integration of electrospinning and atomic layer deposition (ALD) technique. Representative results include the following: 1) ultrathin CoN layer is deposited by ALD on the surface of flexible substrate without any sacrifice of SFCNF and Co1−xS; 2) the composite exhibits strong electrocatalytic activity in both acidic and basic solutions. The overpotentials of hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) are 20 and 180 mV, respectively, at a current density of 10 mA cm−2 in basic medium. A small Tafel slope of 54.4 mV dec−1 is observed in 0.5 m H2SO4 electrolyte; 3) tested as overall water splitting electrode, the composite records a current density of 10 mA cm−2 at a relative low cell voltage of 1.58 V and long‐term stability for 20 h at a current density of up to 50 mA cm−2. The superior performance for overall water splitting is probably attributed to the synergistic effect of Co1−xS and ALD CoN. Specifically, implementation of ALD can be extended to innovate nanostructured materials for overall water splitting and even other renewable energy aspects.
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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) | 0.000 | 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".