Reaching the Fundamental Limitation in CO2 Reduction to CO with Single Atom Catalysts
Bibliographic record
Abstract
The electrochemical CO2 reduction reaction (CO2RR) to value-added chemicals with renewable electricity is a promising method to decarbonise parts of the chemical industry. Recently, single metal atoms in nitrogen-doped carbon (MNC) have emerged as potential electrocatalysts for CO2RR to CO with high activity and faradaic efficiency, although the reaction limitation for CO2RR to CO is unclear. To understand the comparison of intrinsic activity of different MNCs, we synthesized two catalysts through a decoupled two-step synthesis approach of high temperature pyrolysis and low temperature metalation (Fe or Ni). The highly meso-porous structure resulted in the highest reported electrochemical active site utilisation based on in situ nitrite stripping; up to 59±6% for NiNC. Ex-situ X-ray absorption spectroscopy confirmed the penta-coordinated nature of the active sites. The catalysts are amongst the most active in the literature for CO2 reduction to CO. Our density functional theory calculations (DFT) show that their binding to the reaction intermediates approximates to that of Au surfaces. However, we find that the TOFs of the most active catalysts for CO evolution converge, suggesting a fundamental ceiling to the catalytic rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".