Computational study of CO2 solubility in amino acid-based ionic liquids using COSMO-RS
Bibliographic record
Abstract
Abstract. The carbon capture, use, and sequestration (CCUS) techniques are proven to be efficient at lowering the atmospheric concentration of carbon dioxide. Notwithstanding the advances in this area, there are still significant restrictions in carbon dioxide (CO2) capture techniques in industry such as high capital costs, solvent evaporation losses, and low absorption and desorption rates. Ionic liquids (ILs) have received much interest as green solvent due to the benefits of their distinctive properties such as low vapor pressure and their capacity to capture CO2 making them a suitable replacement for present solvents, such as amines. Amino acid based ILs having close similarity with the alkanolamines may potentially have high affinity for CO2 absorption. Nevertheless, available database on these ILs is still limited and only focus on the common types of amino acids. Therefore, this paper aims to predict the CO2 absorption of different amino acid-based ionic liquids as cation/anion using quantum chemical calculation tools namely Conductor like Screening Model for Real Solvents (COSMO-RS) and TURBOMOLE. We evaluated 84 different ILs of different cations and anions based on their CO2 capacity, activity coefficient at infinite dilution (γ∞), and Henry’s constant (H). The results showed that amino acid as anions significantly enhanced the CO2 solubility compared to amino acid as cations. However, glycinium tetrafluoroborate [Gly+][BF4] showed high affinity for CO2 absorption compared to other amino acid-cations based with activity coefficient at infinite dilution (γ∞) = 0.117 and (H) = 8.07. We showed that the selection of anions/cations can significantly change the CO2 capacity in ILs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".