Carbon‐Efficient CO<sub>2</sub> Electrolysis to Ethylene with Nanoporous Hydrophobic Copper
Bibliographic record
Abstract
Abstract Electrochemical carbon dioxide (CO2) reduction offers a low‐carbon route to ethylene, when powered from renewable energy. Yet, the best‐performing CO2 electrolysis systems employ neutral or alkaline electrolytes, resulting in low conversion efficiencies, which increases downstream separation costs. High conversion rates can be achieved with acidic electrolytes, albeit at the cost ethylene faradaic efficiency (FE < 45%). As a result, achieving high ethylene selectivity simultaneously with high conversion efficiency is an unmet challenge. Here, a 3D‐nanoporous catalyst is designed to modulate water and CO2 concentration at the catalyst surface, reaching higher ethylene selectivity in a forward‐bias bipolar MEA that recovers unreacted CO2. An ethylene FE of 63% at 150 mA cm−2 is reported, and by optimizing for conversion efficiency, an ethylene FE of 58% along with an overall conversion efficiency of 82% is achieved. Stable performance for > 65 h at industrial current densities combined with a high ethylene concentration in the product stream showcases promising industrial viability.
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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".