Predicting the Temperature Dependence of the Octanol–Air Partition Ratio: A New Model for Estimating $$\Delta {U^{ \circ}_{\text{OA}}}$$
Bibliographic record
Abstract
Abstract The octanol–air partition ratio (KOA) describes the partitioning of a chemical between air and octanol and is often used to approximate other partitioning phenomena in environmental chemistry (e.g., blood–air, atmospheric particulate matter–air, polyurethane foam-air). Such partitioning processes often occur at environmental temperatures other than 25 °C. Enthalpies $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ or internal energies $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of phase transfer are used to express the temperature dependence of the KOA. Existing poly-parameter linear free energy relationships (ppLFERs) for predicting $$\Delta {H^{ \circ}_{\text{OA}}}$$ Δ H OA ∘ were developed using a relatively small dataset. In this work we utilize a recently developed comprehensive KOA database to create and curate a $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ dataset containing 195 chemicals and use this dataset in the development of new predictive equations. Using the QSAR development platform QSARINS we evaluate the use of Abraham descriptors, other molecular descriptors, and the log10KOA at 25 °C as variables in different multilinear regression equations for $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ . The $$\Delta {U^{ \circ}_{\text{OA}}}$$ Δ U OA ∘ of neutral organic chemicals can be reliably predicted using only the log10KOA (RMSEEXT = 6.86 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2 = 0.94), only the solute’s hydrogen acidity A and the logarithm of the hexadecane–air partition ratio L (RMSEEXT = 7.23 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2 = 0.93), or A and log10KOA (RMSEEXT = 6.76 kJ·mol−1, $${\text{R}^{2} _{\text{adj}}}$$ R adj 2 = 0.95).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".