Selective Electrochemical Production of Ethylene from Bicarbonate Solution
Bibliographic record
Abstract
Abstract Carbon dioxide (CO 2 ) electroreduction directly from a reactive carbon solution (e.g., (bi)carbonate) provides a promising approach for integrating CO 2 capture and conversion. Compared to CO 2 conversion in gas‐fed systems, this system typically suffers from low Faradaic efficiency (FE), especially for multicarbon (C 2+ ) products. Here, we report an engineered material structuring to selectively produce C 2+ products directly from a N 2 ‐saturated bicarbonate solution. Multiphysics modeling studies reveal the critical role of local current density distribution and the spatio‐selective evolution of C 2+ products, which is favored in thinner catalysts (240 µm thickness). By jointly tailoring catalyst configuration and mass transport in bicarbonate electroreduction, adjusting the thickness, porosity, and surface oxidation of copper (Cu) mesh catalysts, as well as catholyte composition, we achieved a maximum C 2 H 4 FE of 39% and total C 2+ FE over 55% at 150 mA cm −2 with a 240 µm thick Cu mesh. The system is also stable for over 160 h at 100 mA cm −2 with maintained C 2 H 4 FE over 20%. Our electrolysis system converts bicarbonate to C 2+ with over 90% CO 2 utilization efficiency, reducing regeneration and separation costs. Optimizing catalyst pore structure, and copper surface oxide is a key to maximizing C 2 H 4 production from bicarbonate solutions.
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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".