Atmospheric Pressure Laser-Induced Acoustic Desorption Chemical Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for the Analysis of Complex Mixtures
Bibliographic record
Abstract
We present a novel nonresonant laser-based matrix-free atmospheric pressure ionization technique, atmospheric pressure laser-induced acoustic desorption chemical ionization (AP/LIAD-CI). The technique decouples analyte desorption from subsequent ionization by reagent ions generated from a corona discharge initiated in ambient air or in the presence of vaporized toluene as a CI dopant at room temperature. Analyte desorption is initiated by a shock wave induced in a titanium foil coated with electrosprayed sample, irradiated from the rear side by high-energy laser pulses. The technique enables facile and independent optimization of the analyte desorption, ionization, and sampling events, for coupling to any mass analyzer with an AP interface. Moreover, the generated analyte ions are efficiently thermalized by collisions with atmospheric gases, thereby reducing fragmentation. We have coupled AP/LIAD-CI to ultrahigh-resolution FT-ICR MS to generate predominantly [M + H](+) or M(+•) ions to resolve and identify thousands of elemental compositions from organic mixtures as complex as petroleum crude oil distillates. Finally, we have optimized the AP/LIAD CI process and investigated ionization mechanisms by systematic variation of placement of the sample, placement of the corona discharge needle, discharge current, gas flow rate, and inclusion of toluene as a dopant.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".