Guide to CO<sub>2</sub> Separations in Imidazolium-Based Room-Temperature Ionic Liquids
Bibliographic record
Abstract
Room-temperature ionic liquids (RTILs) are nonvolatile, tunable solvents that have generated significant interest across a wide variety of engineering applications. The use of RTILs as media for CO 2 separations appears especially promising, with imidazolium-based salts at the center of this research effort. The solubilities of gases, particularly CO 2, N 2, and CH 4, have been studied in a number of RTILs. Process temperature and the chemical structures of the cation and anion have significant impacts on gas solubility and gas pair selectivity. Models based on regular solution theory and group contributions are useful to predict and explain CO 2 solubility and selectivity in imidazolium-based RTILs. In addition to their role as a physical solvent, RTILs might also be used in supported ionic liquid membranes (SILMs) as a highly permeable and selective transport medium. Performance data for SILMs indicates that they exhibit large permeabilities as well as CO 2 /N 2 selectivities that outperform many polymer membranes. Furthermore, the greatest potential of RTILs for CO 2 separations might lie in their ability to chemically capture CO 2 when used in combination with amines. Amines can be tethered to the cation or the anion, or dissolved in RTILs, providing a wide range of chemical solvents for CO 2 capture. However, despite all of their promising features, RTILs do have drawbacks to use in CO 2 separations, which have been overlooked as appropriate comparisons of RTILs to common organic solvents and polymers have not been reported. A thorough summary of the capabilities—and limitations—of imidazolium-based RTILs in CO 2 -based separations with respect to a variety of materials is thus provided.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.056 |
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".