Carbon Dioxide Valorization via Formate Electrosynthesis in a Wide Potential Window
Bibliographic record
Abstract
Abstract The electrochemical CO2 reduction reaction (CO2RR) is a promising strategy to convert CO2 to carbon‐based fuels and to simultaneously reduce the emission of greenhouse gases into the atmosphere. In this work, the bismuth subcarbonate nanoflowers (BOC NFs) are facilely prepared through a one‐pot synthesis method for efficient formate electrosynthesis through CO2RR. Benefiting from the crystal structure and sheet‐stacked morphology, the in situ measurements and theoretical calculation results reveal the self‐reinforced CO2 adsorption properties and rapid CO2 adsorption–desorption kinetics on the catalyst surface, which significantly facilitate the CO2RR process. As a result, the desirable Faradaic efficiencies of over 90%, with a maximum value of 98.9%, toward formate formation, are achieved in a wide potential window from −0.8 to −1.4 V in an H‐type cell. Moreover, in a flow cell, the superior intrinsic activity of BOC NFs guarantees the high throughput electrocatalytic performance of CO2RR and the FEformate of over 90% with high current density is achieved in a potential range as wide as 1200 mV, demonstrating the great potential of BOC NFs for practical CO2RR applications. These results underscore the effectiveness of designing electrocatalysts with self‐reinforced CO2 adsorption properties to improve electrocatalytic performance for efficient 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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".