Determination of Halogenated Flame Retardants Using Gas Chromatography with Atmospheric Pressure Chemical Ionization (APCI) and a High-Resolution Quadrupole Time-of-Flight Mass Spectrometer (HRqTOFMS)
Bibliographic record
Abstract
A method to determine halogenated flame retardants was developed that utilizes gas chromatography with atmospheric chemical ionization (APCI) high-resolution quadrupole time-of-flight mass spectrometry (HRqTOFMS). The new GC-APCI-HRqTOFMS method was used to determine the presence of 65 halogenated flame retardants (HFRs) in the United Sates National Institute of Standards and Technology (NIST) organic contaminants in house dust standard reference material (SRM). The accuracy of the measurements was compared to the certified NIST value for polybrominated diphenyl ethers (PBDEs) and had an average accuracy for the 14 certified PBDEs of 109% with subpicogram detection limits (on column) from a single 1 μL injection with a run time of 18 min. SRM2585 extracts were also analyzed by GC electron ionization (EI) high-resolution mass spectrometry (HRMS), and there was an excellent correlation between the two data sets ( R 2 value of 0.996). The presence of 25 additional HFRs were also screened in the dust standard, and 10 were detected in concentrations above the limits of detection; these were p-TBX, PBBZ, PBT, PBEB, TDCPP, HBBZ, EHTBB, TBBPA, BEHTBP, and BTBPE. The results presented show that the proposed APCI-HRqTOFMS method was comparable and in many cases an improvement on the existing EI-HRMS method.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".