Gas Chromatography Plasma-Assisted Reaction Chemical Ionization Mass Spectrometry for Quantitative Detection of Bromine in Organic Compounds
Bibliographic record
Abstract
We have recently introduced plasma-assisted reaction chemical ionization mass spectrometry (PARCI-MS) for elemental analysis of halogens in organic compounds. Here, we utilize gas chromatography (GC) coupled to PARCI-MS to investigate the mechanism of Br(-) ion generation from organobromines and to evaluate analytical performance of PARCI for organobromine analysis. Bromine atoms in compounds eluting from GC are converted to HBr in a low-pressure microwave induced helium plasma with trace amounts of hydrogen added as a reaction gas. Ionization is achieved by introducing nitrogen into the afterglow region of the plasma, liberating electrons via penning ionization and leading to formation of negative ions. We demonstrate that N2 largely affects the ionization process, whereas H2 affects both the ionization process and in-plasma reactions. Our investigations also suggest that dissociative electron capture is the main ionization route for formation of Br(-) ions. Importantly, GC-PARCI-MS shows a uniform response factor for bromine across brominated compounds of drastically different chemical structures, confirming PARCI's ability to quantify organobromines in the absence of compound-specific standards. Over 3 orders of magnitude linear dynamic range is demonstrated for bromine quantification. We report a detection limit of 29 fg of bromine on-column, ~4-fold better than inductively coupled plasma-MS.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".