Electrocatalytic Upgrade of Impure CO<sub>2</sub> by In Situ-Reconstructed Cu Catalysts with Gas Exsolution Electrolyzers
Bibliographic record
Abstract
Electrochemical upgrading of CO 2 to multicarbon chemicals is widely investigated for carbon neutrality, while the activity of catalysts and the production rate of electrolyzers require further improvements to satisfy industrial demands, especially with impure CO 2 at a low concentration. We propose to employ in situ electrodeposition of Cu on different substrates to improve the activity and selectivity of catalysts and further assemble the electrodes into a customized flow-through electrolyzer to boost the conversion rate. The Cu catalyst on a carbon fabric (CF) substrate demonstrates the highest current density among the controlled samples. This is ascribed to the promoted in situ CO 2 exsolution for carbon supply induced by a CF substrate with interlaced fibers, as proved by the combined pore-scale multiphysics simulation and experimental characterizations. Therefore, Cu catalyst on the CF substrate shows Faradaic efficiency of over 90% for the total carbon products and a current density of over 300 mA·cm –2 at −0.83 V versus RHE. Furthermore, such a customized flow-through cell using an aqueous electrolyte demonstrates a stable and efficient upgrade of CO 2 with both pure and impure CO 2 (90%) inlets, exhibiting auspicious prospects for the industrial application of CO 2 electrolysis.
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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 | 0.000 |
| 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".