QSPRs for Molecular Diffusion Coefficients in Polymeric Passive Samplers: A Comparison of Simple Molecular and Quantum‐mechanical Sigma‐moment Descriptors
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Bibliographic record
Abstract
Abstract Linear quantitative structure‐property relationships (QSPRs) for the prediction of diffusion coefficients (log D p ) were developed for organic contaminants in two common passive sampler materials, polydimethylsiloxane (PDMS) and low‐density polyethylene (LDPE). Literature data was compiled for both PDMS and LDPE resulting in final data sets of 196 and 79 compounds, respectively. Data sets contained compounds with log D p values that ranged over about 5 log units and 3 log units for PDMS and LDPE, respectively. The quality of log D p prediction using either simple molecular descriptors or quantum‐chemical based COSMO‐RS sigma moment descriptors was compared for both materials. For PDMS, the sigma moment descriptor QSPR had the best predictivity with a correlation coefficient of R 2 =0.85 and root mean square error (RMSE) of 0.36 for log D p . The molecular descriptor QSPR resulted in a correlation coefficient of R 2 =0.78 and RMSE of 0.45 for log D p . For LDPE, the molecular descriptor QSPR had the best predictivity, with the final correlation coefficient of R 2 =0.86 and RMSE of 0.21 for log D p . The sigma moment descriptor QSPR resulted in a correlation coefficient of R 2 =0.66 and RMSE of 0.33 for log D p . The purely electronic structure‐based sigma moments are therefore shown to be a viable option for descriptors compared to the more commonly used molecular descriptors for organic contaminants in PDMS. The significance of the descriptors in each QSPR is discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it