Controlled Formation of Protonated and Radical Cation Precursor Ions by Atmospheric Pressure Photoionization with μLC-MS Enables Electron Ionization and MS/MS Library Search
Bibliographic record
Abstract
Atmospheric pressure photoionization (APPI) was developed as an alternative to electrospray ionization (ESI) for the generation of protonated molecules using liquid chromatography and optimized using dopants such as toluene, which predominantly forms protonated molecules, and chlorobenzene, which favors the formation of radical cations, although the latter has not been fully exploited. Based on 40 diverse low-molecular-weight compounds and micro liquid chromatography (μLC) coupled with APPI tandem mass spectrometry (APPI-MS/MS), the potential of radical cations was investigated. Chromatographic and ionization conditions were decoupled by post-column addition of methanol, allowing separate study and optimization. Due to the mass flow sensitive behavior of APPI, sensitivity is not affected by post-column dilution, and for 8 of 35 analytes, the radical cation response with μLC-APPI is better than for protonated molecules using μLC-ESI. Collision-induced fragmentation (CID) of radical cations produced within a collision energy range from 10-115 eV have, in the median, 65% of the fragments found in electron ionization (EI) spectra. This similarity allowed identification of 86% of the analytes using data-dependent acquisition (DDA) of radical cations and NIST EI library searches. We propose a workflow that uses multimodal DDA of protonated precursor molecules using ESI or APPI with toluene as a dopant, and radical cations produced by chlorobenzene-assisted μLC-APPI with post-column addition of methanol. This increases the confidence of molecular identification by allowing orthogonal library searches using MS/MS libraries for protonated precursor CID spectra and EI libraries for radical cation CID spectra.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".