Nontarget and Suspect Screening of Fluorinated Ionic Liquids and PFAS in European Wastewaters Using Supercritical Fluid Chromatography
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Per- and polyfluoroalkyl substances (PFAS) and fluorinated ionic liquids were investigated in municipal effluents from 30 wastewater treatment plants (WWTPs) across 15 European countries using supercritical fluid chromatography–high-resolution mass spectrometry (SFC-HRMS) for nontarget screening. Bis-perfluoroalkyl sulfonimide (bis-FASI) ionic liquids were detected as bis(trifluoromethanesulfonyl)imide (NTf 2 – ), two rarely reported homologues (±2 CF 2, namely FSI – and BETI – ), and two previously unreported homologues (±1 CF 2, namely FTFSI – and FTNTf 2 – ). Bis-FASIs were present in 85% of samples and were more abundant in effluents from larger WWTPs. The fluorinated anion PF 6 –, commonly used in ionic liquids, was found in all samples (≤3 μg/L). Hexafluoroarsenate (AsF 6 – ), reported here for the first time in municipal wastewater, was detected in 32% of samples in eight countries. PF 6 – and AsF 6 – concentrations exceeded those of traditional PFSAs and PFCAs in 97% of the samples. No removal was detected for perfluorinated compounds, inorganic anions, and low-fluorinated pharmaceuticals and pesticides. Low-fluorinated substances were detected in 90% of samples (>100 ng/L), yet PF 6 – alone surpassed the combined concentration of all low-fluorinated substances in 27 out of 30 samples. These results reveal the significance of unconventional fluorinated substances for the overall fluorine load in wastewater, highlighting the need to extend monitoring strategies beyond legacy PFAS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".