Applying the modified UV-activated TOP assay to complex matrices impacted by aqueous film-forming foams
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a large chemical family, and numerous chemical species can co-exist in environmental samples, especially those impacted by aqueous film-forming foams (AFFFs). Given the limited availability of chemical standards, capturing the total amount of PFAS is challenging. Thus, the total oxidizable precursor (TOP) assay has been developed to estimate the total amount of PFAS via the oxidative conversion of precursors into perfluorocarboxylic acids (PFCAs). This study aims to enhance the robustness of the TOP assay by replacing heat activation with UV activation. We evaluated the molar yields of known precursors in water in the presence of varying levels of Suwannee River natural organic matter (SRNOM) and in two soils. The impact of UV activation was also evaluated in two soils spiked with three well-characterized AFFFs, six AFFF-impacted field soils, and nine rinse samples of AFFF-impacted stainless-steel pipe. In the presence of 100 mg/L SNROM, 6:2 fluorotelomer sulfonate (FTS), 8:2 FTS, and N-ethyl perfluorooctane sulfonamidoacetic acid (N-EtFOSAA) in deionized water had good molar recovery as PFCAs (average of 102 ± 9.8 %); at 500 mg/L SNROM, the recovery significantly dropped to an average of 51 ± 19 %. In two soils (with 4 % and 8.8 % organic matter) with individual precursor spikes, the average molar recovery was 101 ± 9.4 %, except N-EtFOSAA, which had a reduced recovery in the soil with 8.8 % organic matter (OM). UV-activated assays outperformed heat-activated ones, especially in AFFF-impacted soils and pipe extract samples, with an average of 1.4-1.5× higher PFCA recovery. In almost all test samples, UV activation resulted in a notable shift towards longer PFCA chain lengths, particularly for samples with high OM content. The study confirmed the advantages of UV activation, including a significantly shortened exposure time (1 h vs. 6 h) and reduced matrix effects from OM due to the dual functions of UV in activating persulfate and photodegrading OM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".