Customizing pyridinic nitrogen coordination in Ni–N–C for electrocatalytic CO<sub>2</sub> reduction towards CO
Bibliographic record
Abstract
Abstract Nickel anchored N-doped carbon electrocatalysts (Ni–N–C) are rapidly developed for the electrochemical reduction reaction of carbon dioxide (CO2RR). However, the high-performanced Ni–N–C analogues design for CO2RR remains bewilderment, for the reason lacking of definite guidance for its structure-activity relationship. Herein, the correlation between the proportion of nitrogen species derived from various nitrogen sources and the CO2RR activity of Ni–N–C is investigated. The x-ray photoelectron spectroscopy (XPS) spectrum combined with the CO2RR performance results show that pyridinic-N content has a positive correlation with CO2RR activity. Moreover, density functional theory (DFT) demonstrates that pyridinic-N coordinated Ni–N4 sites offers optimized free energy and favorable selectivity towards CO2RR compared with pyrrolic-N. Accordingly, Ni–Na–C with highest pyridinic-N content (ammonia as nitrogen source) performs superior CO2RR activity, with the maximum carbon monoxide faradaic efficiency (FECO) of 99.8% at −0.88 V vs. RHE and the FECO surpassing 95% within potential ranging of −0.88 to −1.38 V vs. RHE. The building of this parameter for CO2RR activity of Ni–N–C give instructive forecast for low-cost and highly active CO2RR 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".