Electrochemical exfoliation from an industrial ingot: ultrathin metallic bismuth nanosheets for excellent CO<sub>2</sub> capture and electrocatalytic conversion
Bibliographic record
Abstract
Formic acid (or formate) is a liquid fuel and chemical feedstock, and it is considered as one of the most useful value-added reductive products from electrochemical CO2 conversion. Green metallic Bi nanosheets are believed be a promising candidate for formic acid production in CO2 electroreduction. However, the complexity of their preparation with a low yield hinders their practical application on a large scale. Herein, we report that by using a cheap and commonly used industrial ingot, phase-pure two-dimensional bismuth nanosheets are fabricated on a large scale by a rapid electrochemical cathodic exfoliation method. In addition to featuring abundant active sites, the obtained Bi nanosheets possess exceptionally high adsorption capacity to CO2 compared to its bulk counterpart, resulting in remarkable enhancement in CO2 electroreduction with high selectivity toward formic acid over a wide range of negative potentials, high current density and satisfactory durability. This facile strategy opens a promising avenue for massive fabrication of metallic Bi nanosheets with excellent electrocatalytic performance for large-scale commercial utilization of CO2.
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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) | 0.000 | 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 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".