Localized mass transport channels for electro-upgrade of dilute CO2 toward high-yield C2+ products
Bibliographic record
Abstract
Electrocatalytic upgrade of CO2 offers a promising approach for recycling of global CO2 emissions, facilitating the achievement of carbon neutrality. Nevertheless, direct utilization of practical dilute CO2 is urgently important yet rather difficult, which is hindered by the balance of reaction kinetics and mass transport of CO2 to the catalytic sites. Herein, we propose coordinating the local environment and active catalyst by constructing covalent organic frameworks (COF) on single-atomic In-doped Cu2O (In1@Cu2O) for a high tolerance of CO2 inlet concentrations (15% to 100%). The optimized amounts of COF functionalized by the trifluoromethyl group act as the local CO2/CO diffusion channels via steric confinement effects and C···F electronic effects. Besides, the formation of key intermediates for C2+ products is greatly facilitated by the promoted COOH adsorption. Hence, a total current of 81.7 A is realized in a 4 × 100 cm2 electrolyzer stack with over 770 mmol/h C2+ products at an inlet of dilute CO2. Such a electrode architecture sheds light on the dilute CO2 electrolysis at the potential industrial scale. The continuous dilute CO2 electrolysis is promising for industrial applications but limited by high resistance of mass transport. Here, the authors report a localized mass transport channels coupling CO2 concentrating and converting process to synthesize highly selective and stable C2+ products.
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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 | 0.001 | 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".