Go with CO: A Case for Targeting Carbon Monoxide As a Reactive Carbon Capture Product
Bibliographic record
Abstract
This study is relevant to reactive carbon capture using aqueous alkaline capture solutions, where captured CO 2 is electrochemically released from a capture solution and then upgraded into commodity chemicals in an electrolyzer. The commercial viability of this form of reactive carbon capture demands that the electrolyzer effluent that is returned to the capture unit be sufficiently alkaline to effectively capture CO 2 from air or a point source. Here, we introduce “electron-alkalinity efficiency” (EA%) to correlate OH – production to electrons consumed during the electrolysis of CO 2 . We show that the maximum EA% value for CO production is 100%, but is less than 50% for the production of HCOO –, CH 4, and C 2 H 4 . This outcome implies that the electrolytic production of CO yields the highest CO 2 capture efficiency. To support this claim, we modeled a 1-m 2 electrolyzer producing CO at a current density of 200 mA cm –2, 100% Faradaic efficiency for CO, and 100% CO 2 utilization, resulting in an OH – production rate of 75 mol h –1 . No other CO 2 reduction products (HCOO –, CH 4, and C 2 H 4 ) generate this level of alkalinity without operating at far more extreme current densities or larger scales. We therefore recommend to “go with CO” for reactive carbon capture.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".