Estimation of liquid‐liquid equilibrium of type 2 systems (water + valeric acid + monobasic ester or dibasic ester or alcohol) using SERLAS, SERLAS‐modified, and SERLAS‐integrated
Bibliographic record
Abstract
This paper studies liquid‐liquid equilibrium (LLE) of the type 2 systems (water + valeric acid + dibasic ester or monobasic ester or alcohol) at T = (298.2 ± 0.1) K and p = (101.3 ± 0.7) kPa. Equilibrium distribution of valeric acid onto (water + solvent) two‐phase system is better for more structured diethyl sebacate and ethyl caprylate as compared to less structured diethyl succinate, diethyl malonate, ethyl valerate, and isoamyl alcohol. The two‐phase envelope size and the tie line slope on the phase diagrams are varying as follows: ethyl caprylate > diethyl sebacate > ethyl valerate > diethyl succinate ≈ diethyl malonate > isoamyl alcohol. The SERLAS‐integrated (solvation energy relation for liquid associated systems‐integrated) molecular model with nine physical descriptors, originated from LSER (linear solvation energy relation) principles in conjunction with group‐contribution method, is proposed and applied to the prediction of type 2 LLE properties. By combining SERLAS with UNIFAC‐Dortmund, we are able to get along with a simultaneous impact of both methods for satisfactorily simulating type 2 phase behaviour so long as solvent effects are concerned. SERLAS, SERLAS‐modified, SERLAS‐integrated, and UNIFAC‐original models have been stringently tested for consistency in reproducing phase equilibrium properties with average deviations inferior to 28.8 %, 44.3 %, 21.3 %, and 30.4 %, respectively.
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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 | 0.001 |
| 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 | 0.001 |
| 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".