Multi‐metallic Layered Catalysts for Stable Electrochemical CO<sub>2</sub> Reduction to Formate and Formic Acid
Bibliographic record
Abstract
Abstract Electrochemical CO 2 reduction (ECR) to value‐added products such as formate/formic acid is a promising approach for CO 2 mitigation. Practical ECR requires long‐term stability at industrially relevant reduction rates, which is challenging due to the rapid degradation of most catalysts at high current densities. Herein, we report the development of a bismuth (Bi) gas diffusion electrode on a polytetrafluoroethylene‐based electrically conductive silver (Ag) substrate (Ag@Bi), which exhibits high Faradaic efficiency (FE) for formate of over 90 % in 1 M KOH and 1 M KHCO 3 electrolytes. The catalyst also shows high selectivity of formic acid above 85 % in 1 M NaCl catholyte, which has a bulk pH of 2–3 during ECR, at current densities up to 300 mA cm −2 . In 1 M KHCO 3 condition, Ag@Bi maintains formate FE above 90 % for at least 500 hours at the current density of 100 mA cm −2 . We found that the Ag@Bi catalyst degrades over time due to the leaching of Bi in the NaCl catholyte. To overcome this challenge, we deposited a layer of Ag nanoparticles on the surface of Ag@Bi to form a multi‐layer Ag@Bi/Ag catalyst. This designed catalyst exhibits 300 hours of stability with FE for formic acid ≥70 % at 100 mA cm −2 . Our work establishes a new strategy for achieving the operational longevity of ECR under wide pH conditions, which is critical for practical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".