Electrochemical Reduction of CO2 to Ethylene with 32% Lower Energy at 80% Lower Cost via Coproduction of Glycolic Acid
Bibliographic record
Abstract
We are in a race against time to implement technologies for carbon capture, conversion, and utilization (CCU) to create a closed anthropogenic carbon cycle. Renewable energy powered electrochemical CO 2 reduction (eCO 2 R) to fuels and chemicals is an attractive technology in this context. Here, we demonstrate a strategy to drive economic feasibility of eCO 2 R to ethylene (C 2 H 4 ), the largest produced organic chemical, by coupling with glycerol oxidation on anode. Our gold nano-dendrite anode catalyst demonstrated very high activity (J ~377 mA/cm 2 at 1.2 V vs reversible hydrogen electrode) and selectivity (~50% to glycolic acid (GA)) for glycerol oxidation. The co-electrolysis process demonstrated record high selectivity of ~60% for C 2 H 4 production at a very low cell voltage of ~ 1.7 V, translating to 32% reduction in required energy compared to conventional eCO 2 R with water oxidation reaction on anode. The experimental results were complemented with a detailed technoeconomic analysis that indicated economic feasibility will depend on several factors such as price of organic feed, selectivity of anode electrode, market value of chemicals produced and most importantly cost of separation and purification. Our results indicate that C 2 H 4 produced via conventional eCO 2 R would require electricity price to plummet to <1 cents/kWh to be cost-competitive, while a co-electrolysis process to produce C 2 H 4 and GA will help reduce C 2 H 4 production cost by ~ 80% to ~1.08 $/kg, reaching cost parity at electricity price of 5 cents/kWh. This study may trigger research efforts for design of electrochemical processes with low electricity requirement using cheap industrial waste streams.
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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 | 0.001 |
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".