Comparison of dopants for charge exchange ionization of nonpolar polycyclic aromatic hydrocarbons with reversed-phase LC-APPI-MS
Bibliographic record
Abstract
Atmospheric pressure photoionization (APPI) is capable of ionizing nonpolar compounds in LC/MS, through charge exchange reactions following photoionization of a dopant. Recently, several novel dopants-chlorobenzene, bromobenzene, 2,4-difluoroanisole, and 3-(trifluoromethyl)anisole-have been identified as having properties making them well-suited to serve as dopants for charge exchange ionization under reversed-phase LC conditions. Here, we report the results of experiments comparing their effectiveness to that of established dopants-toluene, anisole, and a toluene/anisole mixture, for the charge exchange ionization of model nonpolar compounds-the 16 polycyclic aromatic hydrocarbons (PAHs) identified by the US EPA as priority pollutants-when using a conventional reversed-phase LC method. Chloro- and bromobenzene were found to be much more effective than toluene for all the PAHs, due to the relatively low reactivity of their photoions with the solvent. Their overall performance was also better than that of anisole, due to anisole's ineffectiveness toward higher-IE compounds. Further, the experiments revealed that anisole's performance for higher-IE compounds can be dramatically improved by introducing it as a dilute solution in toluene, rather than neat. The two fluoroanisoles provided the highest overall sensitivity, by a slim margin, when introduced as dilute solutions in either chloro- or bromobenzene.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".