Gas Chromatography-(Cyclic) Ion Mobility Mass Spectrometry: A Novel Platform for the Discovery of Unknown Per-/Polyfluoroalkyl Substances
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFASs) have been widely used since the 1940s in industry and everyday household products. They also persist in the environment and bioaccumulate in humans and wildlife. Despite these concerns, the identities of most PFASs in environmental and biological samples are unknown. Herein, we describe a novel cyclic ion mobility mass spectrometer (cIMS), hyphenated with gas chromatography (GC) atmospheric pressure chemical ionization, that can reveal the presence of unknown PFASs on the basis of the ratio of their mass and collision cross section (CCS). Prediction of the CCS of ca. 20,000 chemicals used in industry and commerce indicates that most compounds characterized by CCS values that are less than the sum of 100 Å 2 and one-fifth of their mass are either PFASs or polybrominated flame retardants. When this filter is applied to GC-cIMS data collected from a set of 20 indoor dust samples, PFAS compounds are revealed without prior knowledge of their occurrence. Validation of this approach was performed using SRM 2585, a standard reference material of household dust, by comparing the PFASs detected with those (tentatively) identified in previous studies. Chlorofluoro phthalimides tentatively identified previously were confirmed with a synthesized standard. The method also reveals the presence of chlorofluoro n -alkanes as an emerging class of “forever chemicals” that contaminate the indoor 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 | 0.001 | 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".