Evaluation of Three Prediction Methods for Partitioning Coefficients of Organic Solutes between a Long-Chain Aliphatic Alcohol and the Gas Phase as a Function of Temperature
Bibliographic record
Abstract
Temperature-dependent experimental hexadecan-1-ol/air partition coefficients of numerous organic solutes are regressed against molecular interaction parameters to derive a linear solvation energy relationship (LSER) for each experimental temperature. The system constants derived by these regressions are linearly related to reciprocal absolute temperature, allowing their extrapolation to 298.15 K and the establishment of a LSER equation for the hexadecan-1-ol/air partition coefficients at 298.15 K. This equation yields predictions comparable to those of another LSER equation for that parameter that is independently derived by extrapolating the system constants of smaller aliphatic alcohols to the chain length of hexadecan-1-ol. This confirms that the solvation properties of the long-chain alkanols can be extrapolated from those of smaller normal alcohols. Hexadecan-1-ol/air partition coefficients for the same solutes are also predicted using SPARC and calculated from vapor pressure data from the literature and activity coefficients in hexadecan-1-ol predicted by UNIFAC. The SPARC- and UNIFAC-predicted partition coefficients agree well with each other, and also the agreements between predictions and measurements are acceptable considering experimental uncertainty. A comparison of measured and predicted activity coefficients in hexadecan-1-ol reveals that they are not correlated, although they are in the same range and have a similar average. The predictions of the partition coefficients simply succeed because their variability is determined by vapor pressure rather than the activity coefficient, which only varies within an order of magnitude.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".