Bifunctional Gas Diffusion Electrode Enables In Situ Separation and Conversion of CO<sub>2</sub> to Ethylene from Dilute Stream
Bibliographic record
Abstract
Abstract The requirement of concentrated carbon dioxide (CO2) feedstock significantly limits the economic feasibility of electrochemical CO2 reduction (eCO2R) which often involves multiple intermediate processes, including CO2 capture, energy‐intensive regeneration, compression, and transportation. Herein, a bifunctional gas diffusion electrode (BGDE) for separation and eCO2R from a low‐concentration CO2 stream is reported. The BGDE is demonstrated for the selective production of ethylene (C2H4) by combining high‐density‐polyethylene‐derived porous carbon (HPC) as a physisorbent with polycrystalline copper as a conversion catalyst. The BGDE shows substantial tolerance to 10 vol% CO2 exhibiting a Faradaic efficiency of ≈45% toward C2H4 at a current density of 80 mA cm−2, outperforming previous reports that utilized such partial pressure (PCO2 = 0.1 atm and above) and unaltered polycrystalline copper. Molecular dynamics simulation and mixed gas permeability assessment reveal that such selective performance is ensured by high CO2 uptake of the microporous HPC as well as continuous desorption owing to the molecular diffusion and concentration gradient created by the binary flow of CO2 and nitrogen (CO2|N2) within the sorbent boundary. Based on detailed techno‐economic analysis, it is concluded that this in situ process can be economically compelling by precluding the C2H4 production cost associated with the energy‐intensive intermediate steps of the conventional decoupled process.
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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 | 0.001 | 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".