Effects of solvent flow, dopant flow, and lamp current on dopant-assisted atmospheric pressure photoionization (DA-APPI) for LC-MS. Ionizationvia proton transfer
Bibliographic record
Abstract
In this paper, the effects of solvent flow, dopant flow, and lamp power on proton transfer ionization in dopant-assisted (DA) atmospheric pressure photoionization (APPI) are investigated. A broad theoretical framework is presented, describing the primary photoionization process, the formation of protonated-solvent cluster ions, and the balance between analyte ion creation via proton transfer and loss via recombination. The principal experimental test system utilized methanol as the solvent, toluene as the dopant, and acridine as the analyte. Comparisons are made between acridine and a less basic compound, 9-methylanthracene (9-MA). Experimental determinations of the trends in the analyte MH + signal and the total ion current (TIC) with variations in the subject parameters are provided. Experimental results and theory demonstrate that both the analyte signal and the TIC approach asymptotic limits with increases in dopant flow and/or lamp current (two factors which dictate the rate of photoion generation). The data show that these limits are lowered at higher solvent flow rates. These results are attributed to the recombination loss process, the rate of which increases with the second power of ion concentration. We deduce that the recombination rate constant increases with solvent flow rate, a consequence of the growth of ion-solvent clusters. Cluster growth is also believed to be a factor in the dramatic loss of sensitivity for 9-MA that occurs as the solvent flow is raised, because larger protonated-solvent cluster ions have greater solvation energies and may be unreactive with compounds having low gas-phase basicity and/or low solvation energy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".