The Analysis of Halogenated Flame Retardants by GC-HRMS in Environmental Samples
Bibliographic record
Abstract
The analytical conditions required to determine polybrominated diphenylethers (PBDEs) and a variety of other halogenated flame retardants (HFRs) by gas chromatography-high resolution mass spectrometry (HRMS) in environmental samples are reported. HRMS can be used to analyze brominated diphenylethers (BDEs), 2,2',4,4',5,5'-hexabromobiphenyl (BB-153) as well as for a number of other emerging HFRs like allyl 2,4,6-tribromophenyl ether (ATE), 2-bromoallyl 2,4,6-tribromophenyl ether (BATE), 2,3-dibromopropyl 2,4,6-tribromophenyl ether (DPTE), octabromotrimethylphenylindane (OBIND), pentabromoethylbenzene (PBEB), hexabromobenzene (HBB), 1,2-bis (2,4,6-tribromophenoxy) ethane (BTBPE), decabromodiphenylethane (DBDPE), Dechlorane Plus (DP), hexachlorocyclopentadienyl-dibromocyclooctane (HCDBCO), tetrabromoethylcyclohexane (TBECH), 1,2,5,6-tetrabromocylcooctane (TBCO), 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EHTeBB), and bis(2-ethly-1-hexyl)tetrabromophthalate (BEHTBP). The detection in environmental matrices and use of these non-BDE flame retardants is reviewed. A method for the analysis of PBDEs by isotope dilution HRMS and 16 other halogenated compounds primarily used as flame retardants is reported. A survey of selected environmental samples, which included Lake Ontario surface and tributary sediments, municipal wastewater effluent, sludge, and mussel tissues, detected PBDEs, DP, DBDPE, BTBPE, PBEB, BB-153, and HBB.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".