Experimentally Determined Aqueous Diffusion Coefficients of PFAS Using <sup>19</sup>F NMR Diffusion-Ordered Spectroscopy
Bibliographic record
Abstract
Per- and polyfluorinated alkyl substances (PFAS) can be found in nearly every aqueous environmental compartment, including rainwater, snow, surface waters, lakes, and oceans. Despite the global distribution of PFAS in the aquatic environment, little is known regarding their diffusion through aqueous systems. This can be limiting for passive sampling techniques, which depend on accurate diffusion coefficients to relate sampler concentrations of PFAS to system-wide concentrations. Existing methods for the measurement of aqueous diffusivity can be time-consuming, challenging, and subject to error when measuring highly fluorinated surfactants. In the present study, we employ fluorine NMR diffusion-ordered spectroscopy ( 19 F DOSY) to experimentally determine the aqueous diffusion coefficient accurately for 47 PFAS. Aqueous diffusion was found to decrease with increasing fluorinated chain length and increase with the inclusion of ether linkages. The impacts of the ionic strength, temperature, and concentration on the aqueous diffusion of PFAS were also examined. The 19 F NMR DOSY method demonstrates reasonable agreement with literature values where available. Numerous PFAS do not have published aqueous diffusion coefficients, which are reported here for the first time. This data allow passive sampling and environmental modeling methods to be greatly improved for monitoring PFAS in the aquatic environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".