A rapid gas chromatography quadrupole time-of-flight mass spectrometry method for the determination of polycyclic aromatic hydrocarbons and sulfur heterocycles in spilled crude oils
Bibliographic record
Abstract
Spilled crude oil samples contain various toxic compounds including polycyclic aromatic hydrocarbons (PAHs) as well as sulfur heterocycles (PASHs) and their related alkylated forms (APAHs and APASHs). In this study, a method was successfully developed employing a gas chromatography quadrupole time-of-flight (GC-QToF) mass spectrometer to quantitatively analyze both PAHs/APAHs and PASHs/APASHs in these samples. With GC-QToF, the monoisotopic mass of the compounds is distinguished, allowing the PASHs/APASHs to be extracted separately from the PAHs/APAHs in crude oil. A gas chromatography triple quadrupole (GC-MS/MS) mass spectrometer was also used to confirm that a GC-QToF is the preferred instrument for analyzing these compounds. With the use of PASH/APASH standards to determine response correction factors (RCFs) in relation to PAH standards, the developed method is capable of analyzing PAHs, APAHs, PASHs, and APASHs in a single injection. The use of RCFs allowed for the development of a practical polycyclic aromatic carbon (PAC) method for analyzing a total of 77 compounds of the 2 groups in crude oil. This newly developed method was applied to spilled crude oils, demonstrating its potential in toxicological study as well as oil spill forensic investigation.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".