Electrosynthesis of Ethylene from Syngas via Functionalized Carbon
Bibliographic record
Abstract
Electrochemical CO reduction (COR) to ethylene (C₂H₄) offers a carbon- and energy-efficient route to chemical production. However, the use of pure CO as feedstock limits scalability, whereas syngas—produced at ~250 times the volume of pure CO globally—is the more practical industrial feedstock. Achieving high single-pass carbon efficiency (SPCE) in syngas-fed COR systems is essential, as high inlet flow rates E introduce H₂ dilution, increasing downstream separation costs. However, under SPCE-optimized conditions, we observed a decline in C₂H₄ Faradaic efficiency (FE) as CO is mass transport-limited on Cu due to a combination H₂ dilution and insufficient CO supply to the cathode. Here, we address this challenge by introducing a COOH-functionalized active carbon layer that selectively captures and enriches CO near Cu catalyst sites. Guided by operando Raman spectroscopy and DFT analysis, this functional interface enhances CO adsorption and enables sustained C₂H₄ selectivity under CO-diluted conditions. We demonstrate a C₂H₄ FE of 72% and SPCE of 73% in syngas (H₂/CO = 2:1), achieving an energy cost of 203 GJ/ton—comparable to leading systems operating with pure CO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".