Studies of one and two component aerosols using IR/VUV single particle mass spectrometry: Insights into the vaporization process and quantitative limitations
Bibliographic record
Abstract
This paper presents the studies of one and two component particles using a CO(2) laser for vaporization and VUV ionization in an ion trap mass spectrometer. The degree of fragmentation for a one component system was demonstrated to be a function of CO(2) laser energy. In a two component system, the degree of fragmentation was shown to be a function of the particle composition. This observation indicates that the analysis of mixed particles may be far more complicated than anticipated for a two step process with soft vaporization. In addition to showing that fragmentation is a function of CO(2) laser energy and particle composition, we also show that a key parameter that determines the extent of fragmentation is the energy absorbed by the particle during desorption. The ionization delay profile in a one component system is also shown to be strongly dependent on the vaporization energy. In a two component system, the delay profile is shown to strongly depend on the composition of the particle. The combined data suggest that the key parameter that governs the delay profile is the energy absorbed by the particle during desorption. This finding has implications for potential field measurements. Finally, for a two component system where the absorption crosssections are different, the change in the degree of fragmentation with particle composition resulted in a non-linear dependence of ion signal on composition. This makes any attempt at quantification difficult.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".