Using mass defect plots as a discovery tool to identify novel fluoropolymer thermal decomposition products
Bibliographic record
Abstract
Fire events involving halogenated materials, such as plastics and electronics, produce complex mixtures that include unidentified toxic and environmentally persistent contaminants. Ultrahigh-resolution mass spectrometry and mass defect filtering can facilitate compound identification within these complex mixtures. In this study, thermal decomposition products of polychlorotrifluoroethylene (PCTFE, [-CClF-CF2 -]n), a common commercial polymer, were analyzed by Fourier transform ion cyclotron resonance mass spectrometry. Using the mass defect plot as a guide, novel PCTFE thermal decomposition products were identified, including 29 perhalogenated carboxylic acid (PXCA, X = Cl,F) congener classes and 21 chlorine/fluorine substituted polycyclic aromatic hydrocarbon (X-PAH, X = Cl,F) congener classes. This study showcases the complexity of fluoropolymer thermal decomposition and the potential of mass defect filtering to characterize complex environmental samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".