Chromatographic and Ionization Properties of Polybrominated Diphenyl Ethers Using GC/High-Resolution MS with Metastable Atom Bombardment and Electron Impact Ionization
Bibliographic record
Abstract
The chromatographic and ionization properties of 35 polybrominated diphenyl ether (PBDE) congeners were investigated using GC/HRMS with metastable atom bombardment (MAB) and electron impact (EI) ionization. A multiple linear regression model based on bromine substitution patterns and MOPAC calculated physical properties was developed to predict relative GC retention times of individual PBDE congeners. Although five different sources of metastable rare gas atoms (He, N2, Ar, Xe, and Kr) were investigated with MAB ionization, only MAB-N2 provided adequate ionization efficiency and predictability. Because of reduced background noise to the MS detector, MAB-N2 had a lower limit of detection for tetra- and penta-BDEs than EI, despite having a lower sensitivity. Using MAB-N2, the molecular ion was always the base peak, with little fragmentation taking place. Conversely, using EI ionization, the [M - nBr]+ peak (where n = 1-4, depending on the number of Br substituents) was the dominant ion for all PBDE congeners. Multiple linear regression models representing the molecular ion response of PBDE congeners analyzed by GC/ HRMS with MAB-N2 and EI ionization were also developed using the number and type of Br substituents and ionization potentials. A significantly higher level of predictability was obtained for the MAB-N2 response model than for EI.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".