UNIQUAC Interaction Parameters with Closure for Imidazolium Based Ionic Liquid Systems Using Genetic Algorithm
Bibliographic record
Abstract
Abstract Ionic liquids (ILs) are being considered as favourable solvents for liquid‐liquid extraction. Ternary phase equilibria containing the ionic liquid based systems have been reported in the literature for aromatic‐aliphatic‐IL as well as aliphatic‐alcohol‐IL ternary systems. In this work global optimization has been used for the prediction of UNIQUAC interaction parameters for IL based systems. The twin concepts of closure equation and global optimization via Genetic Algorithm (GA) have been benchmarked and tested on 88 aromatic and 28 hydrogen bonding multi‐component systems. For the aromatic systems the rmsd values obtained with closure equation are ∼20 percent better than without closure equation and ∼50 percent better than literature. Similarly for hydrogen bonding systems with closure equation gives ∼20 percent better rmsd values than without closure equations with an overall improvement of ∼60 percent. After this rigorous testing we have applied this procedure on 29 imidazolium based IL ternary systems. Improvements in rmsds with closure have been ∼6 percent better than without closure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".