Discovery of C<sub>5</sub>–C<sub>17</sub>Poly- and Perfluoroalkyl Substances in Water by In-Line SPE-HPLC-Orbitrap with In-Source Fragmentation Flagging
Bibliographic record
Abstract
The presence of unknown organofluorine compounds in environmental samples has prompted the development of nontargeted analytical methods capable of detecting new perfluoroalkyl and polyfluoroalkyl substances (PFASs). By combining high volume injection with high performance liquid chromatography (HPLC) and ultrahigh resolution Orbitrap mass spectrometry, a sensitive (0.003-0.2 ng F/mL for model mass-labeled PFASs) untargeted workflow was developed for discovery and characterization of novel PFASs in water. In the first step, up to 5 mL of water is injected to in-line solid phase extraction, chromatographed by HPLC, and detected by electrospray ionization with mass spectral acquisition in parallel modes cycling back and forth: (i) full scan with ultrahigh resolving power (RP = 120,000, mass accuracy ≤3 ppm), and (ii) in-source fragmentation flagging scans designed to yield marker fragment ions including [C2F5](-) (m/z 118.992), [C3F7](-) (m/z 168.988), [SO4H](-) (m/z 96.959), and [Cl](-) (m/z 34.9). For flagged PFASs, plausible empirical formulas were generated from accurate masses, isotopic patterns, and fragment ions. In the second step, another injection is made to collect high resolution MS/MS spectra of suspect PFAS ions, allowing further confirmation of empirical formulas while also enabling preliminary structural characterization. The method was validated by applying it to an industrial wastewater, and 36 new PFASs were discovered. Of these, 26 were confidently assigned to 3 new PFAS classes that have not previously been reported in the environment: polyfluorinated sulfates (CnFn+3Hn-2SO4(-); n = 5, 7, 9, 11, 13, and 15), chlorine substituted perfluorocarboxylates (ClCnF2nCO2(-); n = 4-11), and hydro substituted perfluorocarboxylates (HCnF2nCO2(-); n = 5-16). Application of the technique to environmental water samples is now warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".