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Record W1544102653 · doi:10.1002/jms.3340

Using mass defect plots as a discovery tool to identify novel fluoropolymer thermal decomposition products

2014· article· en· W1544102653 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Mass Spectrometry · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversity of Toronto
FundersUniversity of Toronto
KeywordsFluoropolymerChemistryMass spectrometryThermal decompositionFourier transform ion cyclotron resonanceDecompositionMass spectrumCongenerAnalytical Chemistry (journal)PolymerOrganic chemistryChromatography

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.023
GPT teacher head0.329
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it