Systematic study of dopants for use in atmospheric pressure photoionization mass spectrometry
Bibliographic record
Abstract
Atmospheric pressure photoionization (APPI) is an effective ionization technique for the analysis of low polarity and nonpolar compounds using liquid chromatography/mass spectrometry. Ions are produced through a mechanism which begins with initial photoionization of a primary reagent, termed a "dopant", followed by either proton transfer or charge exchange with the analyte(s). This thesis regards improving the ionization efficiency of APPI by identifying new dopant candidates that can increase the breadth of compounds amenable to APPI, and/or improve the ionization efficiency for compounds that are already amenable to APPI. The desired properties for a dopant candidate include high ionization energy (IE) and low reactivity of its photoions with solvent and dopant neutrals. Reactivity tests for 25 substituted-benzene compounds with substituents ranging from strongly electron withdrawing (EW) to strongly electron donating (ED) were performed. Results showed that ED groups decreased reactivity and IE while EW groups increased reactivity and IE; an exception was if the ED group was itself acidic. Of the compounds tested, 2,4-difluoroanisole and 3-(trifluoromethyl)anisole showed the best potential as dopants for charge exchange. These dopants - along with two other novel dopants, bromo- and chlorobenzene - were compared with established dopants (toluene, anisole, and a toluene/anisole mixture) for charge exchange ionization of polycyclic aromatic hydrocarbons (PAHs). Bromo- and chlorobenzene both showed significant improvement in ionization efficiency compared with previously established dopants due to their relatively low reactivity with the solvent and high IE. It was also found that the improved performance for higher IE PAHs, when using anisole, diluted to 0.5% with toluene, was possibly due to the presence of an impurity in anisole. Of the dopants tested, bromobenzene/2,4-difluoroanisole (99.5:0.5 v/v) was determined to be the best overall for charge exchange ionization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".