Ammonia Thermal Treatment toward Topological Defects in Porous Carbon for Enhanced Carbon Dioxide Electroreduction
Bibliographic record
Abstract
Abstract Topological defects, with an asymmetric local electronic redistribution, are expected to locally tune the intrinsic catalytic activity of carbon materials. However, it is still challenging to deliberately create high‐density homogeneous topological defects in carbon networks due to the high formation energy. Toward this end, an efficient NH 3 thermal‐treatment strategy is presented for thoroughly removing pyrrolic‐N and pyridinic‐N dopants from N‐enriched porous carbon particles, to create high‐density topological defects. The resultant topological defects are systematically investigated by near‐edge X‐ray absorption fine structure measurements and local density of states analysis, and the defect formation mechanism is revealed by reactive molecular dynamics simulations. Notably, the as‐prepared porous carbon materials possess an enhanced electrocatalytic CO 2 reduction performance, yielding a current density of 2.84 mA cm −2 with Faradaic efficiency of 95.2% for CO generation. Such a result is among the best performances reported for metal‐free CO 2 reduction electrocatalysts. Density functional theory calculations suggest that the edge pentagonal sites are the dominating active centers with the lowest free energy (Δ G ) for CO 2 reduction. This work not only presents deep insights for the defect engineering of carbon‐based materials but also improves the understanding of electrocatalytic CO 2 reduction on carbon defects.
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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".