Isolated Cu-Sn diatomic sites for enhanced electroreduction of CO2 to CO
Bibliographic record
Abstract
Electrochemical CO 2 reduction reaction (CO 2 RR) to high-value product, CO, not only provides a key feedstock for the well-established Fischer—Tropsch process but also mitigates the greenhouse effect. However, it suffers from sluggish reaction kinetics, competitive hydrogen evolution reaction, and low selectivity. Herein, we report non-precious Cu-Sn diatomic sites anchored on nitrogen-doped porous carbon (CuSn/NPC) as an efficient catalyst for CO 2 RR to CO. The catalyst exhibits outstanding selectivity with CO Faradaic efficiency (FE) up to 99.1%, much higher than those of individual Cu (66.2%) and Sn (51.3%) single-atom catalysts. Moreover, high stability is confirmed by consecutive 24 h electrolysis with high selectivity from CO 2 to CO. Theoretical calculations reveal an obvious activation of CO 2 with weakened C—O bonds and distorted CO 2 configuration upon chemisorption on the CuSn/NPC catalyst. It is also suggested CuSn/NPC is more selective for the CO 2 RR with dominant CO production during the electrolysis, rather than the competing hydrogen evolution reaction.
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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".