Deciphering Mesopore-Augmented CO<sub>2</sub> Electroreduction over Atomically Dispersed Fe–N-doped Carbon Catalysts
Bibliographic record
Abstract
Mesoporous metal–nitrogen-doped carbons (M–N–C) have shown remarkable performance as catalysts for electrochemical CO 2 reduction. However, the current understanding of the roles of mesopores in M–N–C-catalyzed CO 2 reduction has been insufficient and imprecise due to the overlooked and intertwined influences of various structural factors on mass transport and the catalyst microenvironment. In this work, we have decoupled the impacts of mesopores in this process by designing Fe–N–C with solely altered pore structures. We found that the mesopore-rich catalyst surpassed its microporous counterpart in the overall reaction rate but unusually fell short in CO selectivity. Our experiments and modulation uncovered that the abundance of mesopores on the catalyst surface facilitated CO 2 diffusion to active sites and thereby improved the CO production rate; however, the increased CO 2 transport buffered the local pH surrounding active sites, which increased H 2 generation and induced a relative decrease in CO selectivity for the mesopore-rich Fe–N–C catalyst.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".