Carbon Dioxide Solubility in Three Bis Tri (Fluromethylsulfonyl) Imide-Based Ionic Liquids
Bibliographic record
Abstract
This study delves into the necessity of mitigating carbon dioxide (CO2) emissions, focusing on effective capture methods to combat global warming by investigating the solubility of CO2 in three ionic liquids (ILs), 1-Decyl-3-MethylimidazoliumBis (Trifluromethylsulfonyl Imide) [IL1], 1-Hexadecyl-3-Methyl imidazoliumBis (Trifluromethylsulfonyl Imide) [IL2] and Triethytetradecyl Ammonium Bis (Trifluromethylsulfonyl Imide) [IL3]. Solubility experiments were conducted at (30, 50 and 70) °C with pressures up to 1.5 MPa. The research shows [IL2] as the superior candidate for CO2 capture, with its longer alkyl chain, and is confirmed by its lower Henry’s Law constant. Utilizing the Peng Robinson equation of state, the study correlates well with the solubility measurements using three mixing rules. The study reveals promising results for IL1, IL2 and IL3 surpassing all other published ionic liquids including Selexol/Genesorb 1753, except for 1-Methyl-3-octylimidazolium bis(trifluoromethylsulfonyl)imide. Insights into the enthalpy and entropy of absorption underscore the significant impact of IL structure on CO2 solubility, emphasizing the potential of tailored ILs for advanced carbon capture strategies. In summary, this research highlights [IL2] as the optimal choice for CO2 capture, offering valuable contributions to the ongoing efforts in combating climate change.
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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 | 0.000 |
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".