Integrated “Two‐in‐One” Strategy for High‐Rate Electrocatalytic CO <sub>2</sub> Reduction to Formate
Bibliographic record
Abstract
Abstract The electrochemical CO 2 reduction reaction (ECR) is a promising pathway to producing valuable chemicals and fuels. Despite extensive studies reported, improving CO 2 adsorption for local CO 2 enrichment or water dissociation to generate sufficient H* is still not enough to achieve industrial‐relevant current densities. Herein, we report a “two‐in‐one” catalyst, defective Bi nanosheets modified by CrO x (Bi−CrO x ), to simultaneously promote CO 2 adsorption and water dissociation, thereby enhancing the activity and selectivity of ECR to formate. The Bi−CrO x exhibits an excellent Faradaic efficiency (≈100 %) in a wide potential range from −0.4 to −0.9 V. In addition, it achieves a remarkable formate partial current density of 687 mA cm −2 at a moderate potential of −0.9 V without iR compensation, the highest value at −0.9 V reported so far. Control experiments and theoretical simulations revealed that the defective Bi facilitates CO 2 adsorption/activation while the CrO x accounts for enhancing the protonation process via accelerating H 2 O dissociation. This work presents a pathway to boosting formate production through tuning CO 2 and H 2 O species at the same time.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".