Air–water partition coefficients for a suite of polycyclic aromatic and other C<sub>10</sub>through C<sub>20</sub>unsaturated hydrocarbons
Bibliographic record
Abstract
The air-water partition coefficients (Kaw) for 86 large polycyclic aromatic hydrocarbons and their unsaturated relatives were estimated using high-level G4(MP2) gas and aqueous phase calculations with the SMD, IEFPCM-UFF, and CPCM solvation models. An extensive method validation effort was undertaken which involved confirming that, via comparisons to experimental enthalpies of formation, gas-phase energies at the G4(MP2) level for the compounds of interest were at or near thermochemical accuracy. Investigations of the three solvation models using a range of neutral and ionic compounds suggested that while no clear preferential solvation model could be chosen in advance for accurate Kaw estimates of the target compounds, the employment of increasingly higher levels of theory would result in lower Kaw errors. Subsequent calculations on the polycyclic aromatic and unsaturated hydrocarbons at the G4(MP2) level revealed excellent agreement for the IEFPCM-UFF and CPCM models against limited available experimental data. The IEFPCM-UFF-G4(MP2) and CPCM-G4(MP2) solvation energy calculation approaches are anticipated to give Kaw estimates within typical experimental ranges, each having general Kaw errors of less than 0.5 log10 units. When applied to other large organic compounds, the method should allow development of a broad and reliable Kaw database for multimedia environmental modeling efforts on various contaminants.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".