Scaled CO Electroreduction to Alcohols
Bibliographic record
Abstract
Electrocatalysis offers a promising route to convert CO2 into alcohols, which is most efficient in a two-step cascade reaction with CO2-to-CO followed by CO-to-alcohol. However, current alcohol-producing CO2/CO electrolyzers suffer from low selectivity or alcohol crossover, resulting in alcohol concentrations of less than 1%, which are further diluted in downstream cold-traps. As a result, electrocatalytic alcohol production has yet to be scaled beyond the lab (1-10 cm2). Here, we reverse the electroosmotic drag of water using a cation exchange membrane assembly, enabling the recovery of over 85% of alcohol products at a concentration of 6 wt.%. We develop a multi-step condenser strategy to separate the produced alcohols from the effluent gas stream without dilution. Scaling up this approach to an 800 cm2 cell resulted in an output of 200 mL alcohol/day. The electrocatalytic upgrading of CO2/CO provides a promising route to produce carbon-neutral alcohols but suffers from product loss to crossover and dilution. Here, the authors report on a CO reduction electrolyzer that recovers over 85% of alcohol without dilution, which is then scaled to 800 cm2.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".