2D Metal Oxyhalide‐Derived Catalysts for Efficient CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Abstract Electrochemical reduction of CO 2 is a compelling route to store renewable electricity in the form of carbon‐based fuels. Efficient electrochemical reduction of CO 2 requires catalysts that combine high activity, high selectivity, and low overpotential. Extensive surface reconstruction of metal catalysts under high productivity operating conditions (high current densities, reducing potentials, and variable pH) renders the realization of tailored catalysts that maximize the exposure of the most favorable facets, the number of active sites, and the oxidation state all the more challenging. Earth‐abundant transition metals such as tin, bismuth, and lead have been proven stable and product‐specific, but exhibit limited partial current densities. Here, a strategy that employs bismuth oxyhalides as a template from which 2D bismuth‐based catalysts are derived is reported. The BiOBr‐templated catalyst exhibits a preferential exposure of highly active Bi ( ) facets. Thereby, the CO 2 reduction reaction selectivity is increased to over 90% Faradaic efficiency and simultaneously stable current densities of up to 200 mA cm −2 are achieved—more than a twofold increase in the production of the energy‐storage liquid formic acid compared to previous best Bi catalysts.
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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".