Method Development for Quantitative Analysis of Polycyclic Aromatic Hydrocarbons, Nitrogen Heterocycles and Sulfur Heterocycles in Crude Oils Using Quadrupole Time-of-Flight Mass Spectrometry
Bibliographic record
Abstract
An oil spill is a catastrophic event that results in various toxic polycyclic aromatic hydrocarbons (PAHs) entering the environment. Polycyclic aromatic nitrogen heterocycles (PANHs) are more toxic to the environment than their parent PAHs. The high cost and paucity of available PANH standards, the lower abundance of PANHs relative to PAHs, and the difficult separation due to co-elution with PAHs have all contributed to the scarcity of related published literature on the determination of these compounds. To overcome these challenges, a new quantitative method has been successfully developed and validated for the inclusion of 113 polycyclic aromatic carbon (PAC) compounds in a single injection. The 113 compounds consist of PAHs, nitrogen heterocycles, sulfur heterocycles, and alkylated equivalents. Distinct separation of the PANHs and their alkylated counterparts (APANHs) from PAHs was achieved using a gas chromatography quadrupole time-of-flight (GC-QToF) mass spectrometer. The instrument resolved compounds by the high-resolution extraction of monoisotopic masses, allowing response correction factors (RCFs) to be determined from available PANH standards and to calculate concentrations from PAH calibration standards. The developed method was applicable to crude oil samples, generating concentrations of PANHs and relevant information on compound stability for use in oil spill forensics investigation. Development of this practical PAC method provides a powerful tool for screening toxic contaminants, assessing environmental impact, and monitoring recovery following an oil spill.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".