<scp>CrN</scp> nanoparticles confined into nitrogen‐doped hierarchical porous carbon matrix for efficient electrocatalytic nitrogen fixation
Bibliographic record
Abstract
Abstract Nitrogen reduction reaction (NRR) driven electrochemical ammonia synthesis via utilizing an inexpensive and efficient electrocatalyst has been confirmed as a potential alternative approach for industrially applied Haber–Bosch process. Chromium nitride‐based nanocomposite (CrN@NC) has been synthesized by a one‐pot pyrolysis and nitriding strategy with tetradecyl trimethyl ammonium bromide (TTAB) as carbon and nitrogen source. In this nanocomposite, CrN nanoparticles are highly dispersed in hierarchical porous nitrogen‐doped carbon matrix. CrN@NC features rich active sites, increased surface area, and enhanced conductivity. Benefitting from its desirable structure, as an inexpensive electrocatalyst for nitrogen fixation, CrN@NC catalyst exhibits an obviously enhanced NRR performance in comparison to the bare CrN. CrN@NC can present a maximal NH 3 production rate about 24.99 μg mg −1 cat h −1 at a low overpotential of −0.2 V versus RHE in Na 2 SO 4 solution, followed by a Faradic efficiency (FE) of 13.53%. Moreover, CrN@NC also exhibits a satisfactory selectivity because of the absence of the detectable hydrazine byproduct.
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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".