Atmospheric pressure photoionization proton transfer for complex organic mixtures investigated by fourier transform ion cyclotron resonance mass spectrometry
Bibliographic record
Abstract
To further clarify the role of dopant solvent in proton transfer in atmospheric pressure photoionization (APPI), we employ ultrahigh-resolution FT-ICR mass analysis to identify M(+*), [M + H](+), [M - H](-), and [M + D](+) species in toluene or perdeuterotoluene for an equimolar mixture of five pyrrolic and pyridinic nitrogen heterocyclic model compounds, as well as for a complex organic mixture (Canadian Athabasca bitumen middle distillate). In the petroleum sample, the protons in the [M + H](+) species originate primarily from other components of the mixture itself, rather than from the toluene dopant. In contrast to electrospray ionization, in which basic (e.g., pyridinic) species protonate to form [M + H](+) positive ions and acidic (e.g., pyrrolic) species deprotonate to form [M - H](-) negative ions, APPI generates ions from both basic and acidic species in a single positive-ion mass spectrum. Ultrahigh-resolution mass analysis (in this work, m/Deltam(50%) = 500,000, in which Deltam(50%) is the mass spectral peak full width at half-maximum peak height) is needed to distinguish various close mass doublets: (13)C versus (12)CH (4.5 mDa), (13)CH versus (12)CD (2.9 mDa), and H(2) versus D (1.5 mDa).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".