CO2 -binding organic liquids (CO2 BOLs) for post-combustion CO2 capture
Bibliographic record
Abstract
CO2-binding organic liquids (CO2BOLs) chemically bind and release CO2 more efficiently than aqueous alkanolamine systems. CO2BOLs are comprised of alcohols and organic amidine or guanidine bases, which chemically bind CO2 as liquid amidinium or guanidinium alkylcarbonate salts. CO2BOLs have high CO2 binding capacities (19% by weight, 147 g CO2/L) compared to that of 30% monoethanolamine solution in water (7% by weight, 108 g CO2/L) because they are liquid with or without bound CO2 and do not require any added solvent such as water. The dissolution of CO2 into and out of the liquid phase limits the rate of CO2 capture and release. Absorption of CO2 is selective over nitrogen in both concentrated and dilute gas streams making these systems applicable to post- and pre-combustion CO2 capture. The free energy of CO2 binding in these systems is small and is independent of the choice of alcohol. The free energies of these systems are dependent on the choice of base; −9 kJ/mol for diazabicyclo[5.4.0]undec-7-ene (DBU) and Barton’s base and +2 kJ/mol for 1,1,3,3-tetramethylguanidine. The specific heats of the organic CO2BOLs are over 50% lower than that of water, resulting in a 50% reduction in the energy needed to strip out CO2 as compared to aqueous alkanolamine solutions. CO2BOLs have been recycled for five cycles without losing activity or selectivity towards CO2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".