Carbon and Energy Efficient Ethanol Electrosynthesis By Acidic CO<sub>2</sub> Reduction
Bibliographic record
Abstract
The electrochemical CO2 reduction reaction (CO2RR) is one way of mitigating the rising levels of anthropogenic CO2 emissions. Most of the advancements in the CO2RR field have been implemented in basic or neutral electrolytes where a major fraction of the input CO2 converts to carbonate ions through reaction with OH– (Science 360, 783-787 (2018); Nat. Catal. 5, 564-570 (2022)). These approaches result in low carbon efficiency (the percentage of CO2 converted per total CO2 input), typically below 20% toward multicarbon products, resulting in severe energy and cost penalty (Nat. Sustain. 5, 563-573 (2022)). Performing CO2RR in acidic conditions reduces reactant loss to carbonates. In this condition, the (bi)carbonate ion crossover/formation is countered by the high proton concentration in the electrolyte. However, the unshielded cathode surface favors the hydrogen evolution reaction (HER) over CO2RR due to the high availability of protons and the fast kinetics of the HER (Science 372, 1074-1078, (2021); Nat. Catal. 4, 654-662 (2021)). Here, we have developed an interfacial cation matrix (ICM) to modulate the local microenvironment on the cathode surface. The ICM increases the local pH and electric field, promoting multicarbon production. Additionally, we have designed a Copper-Silver catalyst to tune the selectivity towards alcohol production. We improved the ethanol FE to 45% at 200 mA/cm2, which, to our knowledge, represents the highestethanol FE in membrane electrode assembly literature. The system maintains a high carbon efficiency (60%) resulting in a total energy cost of 263 GJ/tonne ethanol, which is the lowest value among current ethanol-producing CO2 electrolysers.
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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) | 0.002 | 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".