Electrospray ionization Fourier transform mass spectrometry of polycyclic aromatic hydrocarbons using silver(I)-mediated ionization
Bibliographic record
Abstract
Here, a methodology employing doped Ag(I) salt as an in situ cationization reagent for efficient ionization of nonpolar molecules within a conventional electrospray ionization source is described. The effectiveness of Ag(I)-mediated ionization is demonstrated using ESI Fourier transform mass spectrometry for the rapid detection and identification of priority pollutant polyaromatic hydrocarbon (PAH) species. In contrast to earlier coordination ESI-MS reports employing silver salts, argentated species are not typically observed for PAH species. Instead, oxidation of the PAH occurs to produce only the [PAH]+· odd-electron molecular parent ion, simplifying spectral analysis. In addition, the method demonstrates linear quantitative performance. The Ag(I) reagent provides quantifiable PAHs (not ordinarily amenable to ESI-MS) from 64 ppb, and suggests the immediate potential for sampling and on-line monitoring of complex, real world, and otherwise intractable environmental samples. Finally, the high mass accuracy of ESI Fourier transform mass spectrometry further allows unequivocal identification of molecular formulas within PAH mixtures.Key words: electrospray ionization, nonpolar, hydrocarbons, polyaromatic, Fourier transform mass spectrometry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".