Studies on the positive-ion mass spectra from atmospheric pressure chemical ionization of gases and solvents used in liquid chromatography and direct liquid injection
Bibliographic record
Abstract
Detailed studies have been made using different source gases and solvents in a Micromass Quattro mass spectrometer under positive ion atmospheric pressure chemical ionization conditions. The major background ions from nitrogen, air, or carbon dioxide were investigated by tandem mass spectrometry, followed by similar studies on solvents commonly employed in normal- and reversed-phase high-performance liquid chromatography, namely, water-acetonitrile, acetonitrile, and dichloromethane, with nitrogen, air, or carbon dioxide; hydrocarbon solvents were studied using nitrogen. Spectra were interpreted in terms of the gases, solvents, and their impurities. The acetonitrile spectra provided clear evidence for both charge exchange and proton transfer, the former being facilitated by the introduction of some air into a flow of nitrogen. Radical cations of acetonitrile dimers, trimers, and tetramers were observed, as were protonated dimer and trimer species. Examination of the analytical response of four polycyclic aromatic hydrocarbons in various hydrocarbon solvents, with nitrogen gas, showed that the sensitivity of detection for an analyte and its ionization mechanism are dependent on both the analyte structure and the solvent, with pyrene showing the highest sensitivity, phenanthrene and fluorene being intermediate, and naphthalene having the lowest sensitivity. The degree of protonation followed the same trend. Signal intensity and degree of protonation were dependent on the alkane solvent used, with isooctane providing the best overall sensitivity for the sum of protonated molecules and molecular ions. The ions observed in these studies appeared to be the most stable ions formed under equilibrium conditions in the source.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".