Collection of Airborne Fluorinated Organics and Analysis by Gas Chromatography/Chemical Ionization Mass Spectrometry
Bibliographic record
Abstract
The ubiquitous detection of perfluorooctane sulfonate (PFOS) in humans and animals has produced a need for sensitive and compound-specific analytical methods to determine the environmental distribution of fluorinated organic contaminants. A suite of potential PFOS precursors (sulfonamides) and fluorotelomer alcohols (FTOHs) were separated by gas chromatography and detected by chemical ionization mass spectrometry (GC/CI-MS). Full-scan spectra were collected in both positive and negative chemical ionization (PCI and NCI, respectively) mode to determine retention time windows and fragmentation patterns. In selected ion monitoring (SIM) mode, instrumental detection limits ranged from 0.2 to 20 pg for individual analytes, depending on ionization mode. PCI mode was preferred for routine analysis because of the simple mass spectra produced, typified by the presence of a major molecular ion [M + H]+. High-volume air samplers collected gaseous and particle-bound fluoroorganics on composite media consisting of XAD-2, polyurethane foam (PUF), and quartz-fiber filters. The combined collection efficiency for individual analytes was 87 to 136% in breakthrough experiments. Application of the method to the analysis of ambient air from urban and rural sites confirmed the presence of six novel fluorinated atmospheric contaminants at picogram per meter3 concentrations. Low concentrations of fluoroorganics were consistently detected in blanks (<4 pg m(-3)); however, this did not prevent confirmation or quantification of environmental concentrations.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".