Selective detection of nitrated polycyclic aromatic hydrocarbons by electrospray ionization mass spectrometry and constant neutral loss scanning
Bibliographic record
Abstract
The development of a method for selective detection of nitrated polycyclic aromatic hydrocarbons (nitro-PAHs) among other polycyclic aromatic compounds (PACs) is described. The method is based on electrospray ionization mass spectrometry (ESI-MS), performed with a triple quadrupole analyzer and constant neutral loss (CNL) scanning. When subjected to ESI conditions, nitro-PAHs give rise to M(-), [M - H](-) and [M - H + 16](-) ions, which in turn produce fragments by losing 30 u, most probably NO. Other PACs do not undergo such fragmentations, and these differences can be exploited for selective detection of nitro-PAHs among other PACs. Nitro-PAHs can therefore be monitored through the loss of 30 u occurring under negative ion mode ESI conditions. Toward the full development of a screening method for nitro-PAHs, this article first discusses some general aspects of the negative ion mode full-scan ESI mass spectra obtained for these compounds and other PAH derivatives. Because the extent of observation of the loss of 30 u is sensitive to the ESI conditions used, the effects of ionization parameters such as solvent used, declustering voltage, and solvent flow rate are evaluated and discussed. Setting these parameters is very important, especially when interfacing a high performance liquid chromatography (HPLC) system with the ESI source of a triple quadrupole mass spectrometer. Preliminary results of on-line microbore HPLC/ESI-MS separations of PAC standards are presented, and elution/ionization conditions discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".