Application of SPME for Comprehensive Analysis of Aerosol Samples
Bibliographic record
Abstract
The importance of comprehensive investigation of aerosol samples relies on the fact that some portions of analytes can be adsorbed on the particles/droplets and for full characterization, study of particle/droplet-bound compounds as well as free, gas-phase ones is required. Among various microextraction techniques, needle-trap devices (NTD) have the capability of trapping particles/droplets and extracting gaseous compounds, simultaneously. However, the filtration efficiency of sorbent-packed NTD can be low which can be improved by adding a filter. In this chapter, the investigation of aerosol samples using filter-incorporated NTD is explained. The application of the device for the study of various aerosol samples such as breath composition, air pollution, and sprays is described. From the reported results from this area, it is shown that free and total concentrations of analytes can vary significantly, depending on the physicochemical properties of the analytes and characteristics of the sample. The results from critical aerosol samples (including breath samples and air pollution) revealed that less-volatile and polar compounds have higher tendencies to remain attached/adsorbed on the particles/droplets. It can be concluded that when only the gas-phase is studied in aerosol samples, a portion of analytes can remain hidden and their related information can be lost from results.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".