Ni<sub>3</sub>C/Ni<sub>3</sub>S<sub>2</sub> Heterojunction Electrocatalyst for Efficient Methanol Oxidation via Dual Anion Co‐modulation Strategy
Bibliographic record
Abstract
Abstract Enhancing active states on the catalyst surface by modulating the adsorption–desorption properties of reactant species is crucial to optimizing the electrocatalytic activity of transition metal‐based nanostructured materials. In this work, an efficient optimization strategy is proposed by co‐modulating the dual anions (C and S) in Ni3C/Ni3S2, the heterostructured electrocatalyst, which is prepared via a simple hot‐injection method. The presence of Ni3C/Ni3S2 heterojunctions accelerates the charge carrier transfer and promotes the generation of active sites, enabling the heterostructured electrocatalyst to achieve current densities of 10/100 mA cm−2 at 1.37 V/1.53 V. The Faradaic efficiencies for formate production coupled with hydrogen evolution approach 100%, accompanied with a stability record of 350 h. Additionally, operando electrochemical impedance spectroscopy (EIS), in situ Raman spectroscopy, and density functional theory (DFT) calculations further demonstrate that the creation of Ni3C/Ni3S2 heterointerfaces originating from dual anions’ (C and S) differentiation is effective in adjusting the d‐band center of active Ni atoms, promoting the generation of active sites, as well as optimizing the adsorption and desorption of reaction intermediates. This dual anions co‐modulation strategy to stable heterostructure provides a general route for constructing high‐performance transition metal‐based electrocatalysts.
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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".