Gas Chromatographic Estimation of Vapor Pressures and Octanol–Air Partition Coefficients of Semivolatile Organic Compounds of Emerging Concern
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The subcooled liquid-phase vapor pressures ( p L 298 /Pa) and octanol–air partition coefficients ( K OA 298 ) at T /K = 298, enthalpies of vaporization (Δ VAP H /kJ·mol –1 ), and internal energies of phase transfer from octanol to air (Δ OA U /kJ·mol –1 ) were estimated for synthetic musks, novel brominated flame retardants (N-BFR), organophosphate esters, and ultraviolet filters using the capillary gas chromatographic retention time (GC-RT) method. These compounds, which spanned approximately six and three orders of magnitude for p L 298 /Pa and K OA 298, respectively, were co-chromatographed with one of three reference compounds to give initial estimates of properties at T /K = 298. The initial GC-RT property estimates were subsequently calibrated using 18 compounds that spanned 6 log units for p L 298 /Pa and 13 compounds covering 4 log units for K OA 298 . The calibrated log 10 p L 298 /Pa values estimated here ranged from 0.14 ± 0.19 to −9.19 ± 0.29 for cyclopentadecanone to syn -dechlorane plus ( syn -DDC-CO), respectively, while the range of log 10 K OA 298 values was 6.59 ± 0.26 to 11.40 ± 0.23 for cyclopentadecanone to 2,2′,4,4′,5-pentabromodiphenyl ether (BDE-99), respectively. The calibrated GC-RT-derived values were highly correlated with, and were within an average of 0.70 log units of, the literature data for compounds with well-established p L 298 /Pa and K OA 298 measured or derived using non-GC-RT methods. Nonpolar compounds were used in this study to estimate the target polar compound data, which may introduce systematic errors. However, the comparison of our GC-RT results against the literature non-GC-RT values shows that the GC-RT methods performed similarly well for estimating both polar and nonpolar target compounds studied in this work.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".