Switchable CO<sub>2</sub> Electroreduction Induced By the Bismuth Moiety with Tunable Local Structures on Graphene
Bibliographic record
Abstract
Controlling the chemical environment of the atomically dispersed central atoms doped in the graphene lattice is critical to achieve desirable catalytic performances in carbon dioxide reduction reaction (CO2RR), however, how the local structures of non-transition metal-based single atom (SAs) affect the catalytic performances of CO2RR remains less understood. This study reports the immobilization of bismuth single atom catalysts (SACs) on pristine and nitrogenated graphene nanosheets with switchable catalytic selectivity in the carbon dioxide reduction reaction (CO2RR). Based on systematic physical characterizations and electrochemical analysis, it has been demonstrated that the Bi atom coordinated with four adjacent nitrogen atoms (Bi-N-C) selectively produces carbon monoxide (CO) with high selectivity at low overpotential, whereas Bi SACs bounded with carbon atoms (Bi-C) almost exclusively generate formate (FA). Theoretical investigations reveal that the Bi-N-C catalyst displays the lowest activation barrier for the first hydrogenation step of CO2 to produce *COOH, while Bi-C shows the most preferable pathway towards the formation of *OCHO, which is considered as the important intermediate species to generate FA. The controllable product distributions are dictated by the different local structures of Bi centers in Bi-N-C and Bi-C, and such differences could induce distinct electronic properties of Bi centers and subsequently switch the CO2RR products from CO to FA. This work has substantiated the importance of the fine-regulation of the coordination environment of one of the representative p-blocking SAs to steer the selectivity of CO2RR.
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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".