Insight into the metal-free electrocatalysis of heteroatom-doped carbon nanocages in competitive CO <sub>2</sub> reduction and H <sub>2</sub> evolution
Bibliographic record
Abstract
Metal-free carbon-based catalysts exhibit diverse electrocatalytic performances in CO2 reduction reaction (CO2RR), but the attributions and contributions of active sites are still confusing to date. Herein, the hierarchical carbon nanocages (hCNC) doped with different heteroatoms (B, N, P, S) are prepared to examine the impact of dopants on the competitive CO2RR and hydrogen evolution reaction (HER). The hCNC and P-doped hCNC show little CO2RR activity, B- and S-doped hCNC show weak CO2RR activity, while N-doped hCNC presents high CO2RR activity. The CO Faradaic efficiency (FECO) of N-containing hCNC increases almost linearly with increasing the N content, even with the co-existing B or P. S- and SN-doped hCNC more facilitate the HER. 16 doping configurations are constructed, and up to 53 sites are examined for the electrochemical activities with a constant potential modelling method. The pyridinic-N(N*) is the best active site for CO2RR to CO, while CBO2H2-1(αC*), CBO2H2-2(γC*), NO-1(βC*), PO2H-3(αC*) and SO3H-3(δC*) are active for HER. The optimized FECO achieves 83.6% for N-doped hCNC with 9.54 at.% nitrogen, and S-doped hCNC reaches ca. 30 mA·cm−2 current density for HER. This study unveils the structure-performance correlation of heteroatom-doped hCNC, which is conducive to the rational design of advanced metal-free carbon-based catalysts.
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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".