Sampling and Raman Confocal Microspectroscopic Analysis of Airborne Particulate Matter Using Poly(dimethylsiloxane) Solid-Phase Microextraction Fibers
Bibliographic record
Abstract
Commercial poly(dimethylsiloxane) (PDMS) 7-microm solid-phase microextraction (SPME) fibers were used for sampling and Raman spectroscopic analysis of a tailpipe diesel exhaust, candle smoke, cigarette smoke, and asbestos dust. Samples were collected via direct exposure of the SPME fiber to contaminated air. The mass loading for SPME fibers was varied by changing the sampling time. Results indicate that PDMS-coated fibers provide a simple, fast, reusable, and cost-effective air sampling tool for airborne particulates. The PDMS coating was stable; Raman bands of the PDMS coating were observed exactly at the same wavenumber positions before and after air sampling. Raman spectroscopic analysis resulted in identification of several characteristic bands allowing chemical speciation of particulates. The advantage of the SPME fiber is the open bed geometry allowing for application of various spectroscopic methods of particulate analysis. This paper describes the first-ever combined application of SPME technology with Raman confocal microspectroscopy for sampling and analysis of airborne particulates. Advantages of the combination of solid-phase microextraction and Raman microspectroscopy for airborne particulate analysis are discussed. Challenges associated with combined SPME sampling and Raman analysis of single particles are also described.
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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) | 0.000 | 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".