Separation of propylene and propane by functional mixture of imidazolium thiocyanate ionic liquid‐organic solvent‐cuprous salt
Bibliographic record
Abstract
Abstract This work presented the preparation of two new systems comprised of ionic liquids (ILs), organic solvent, and cuprous salt. Consequently, their absorption ability was investigated for propylene (C3H6) and propane (C3H8) and their mixtures at 100 kPa‐700 kPa pressure and 298 K‐318 K temperature. Representative ILs were 1‐butyl‐3‐methylimidazolium thiocyanate ([C4mim]SCN) and 1‐ethyl‐3‐methylimidazolium thiocyanate ([C2mim]SCN). N,N‐dimethylformamide (DMF) and cuprous thiocyanate (CuSCN) were selected for the absorption system. The effects of operating parameters like Cu+ concentration, pressure, temperature, and recycling of ILs absorbency were examined and compared with the literature. It was observed that C3H6 shows a chemical absorption while C3H8 undergoes a physical one by [C4mim]SCN‐DMF‐CuSCN and [C2mim]SCN‐DMF‐CuSCN, and an increase in concentration of Cu + effectively improves the absorption capability for C3H6 and the selectivity of C3H6/C3H8. Furthermore, [C4mim]SCN‐DMF‐CuSCN had higher absorption capability and selectivity for C3H6 than [C2mim]SCN‐DMF‐CuSCN. [C4mim]SCN‐CuSCN‐2.0 M absorbed 0.637 mol of C3H6 per litre while 0.16 mol of C3H8 per litre at 700 kPa and 298 K, with a selectivity of 3.9. This work shows that new combination of organic solvent and ILs with Cu+ salt is a new class of potential reactive absorbents to separate C3H6 and C3H8.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".