Unusual Fragmentations of Silylated Polyfluoroalkyl Compounds Induced by Electron Ionization
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent compounds that present analytical challenges due to their stability and low concentrations. In this study, electron ionization (EI) mass spectra of trimethylsilyl (TMS) derivatized fluorinated alcohols and carboxylic acids were examined to improve PFAS identification in the NIST Mass Spectral Reference Library. In contrast with the spectra of unsubstituted alcohol TMS compounds featuring losses of hydrocarbons, fluorinated alcohol TMS derivatives are characterized by the losses of fluorinated silyl groups. For example, a previously unreported [M–111] + ion was consistently observed in compounds containing three methylene groups between the hydroxyl group and the first CF 2 unit. Detailed quality assurance analysis using a suite of NIST software tools along with high-resolution TOF-MS confirmed the origin and elemental composition of these ions. MS 2 experiments and full scan of TMS derivatives of fluorinated alcohols with varying numbers of methylene groups investigations suggest the formation of a five-membered ring intermediate as a key feature in this unique fragmentation pathway. These findings improve our understanding of PFAS fragmentation and support more accurate compound identification in analytical workflows.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".