Design and Validation of Portable SPME Devices for Rapid Field Air Sampling and Diffusion-Based Calibration
Bibliographic record
Abstract
The use of SPME fibers coated with porous polymer solid phases for quantitative purposes is limited due to effects such as interanalyte displacement and competitive adsorption. For air analysis, these problems can be averted by employing short exposure times to air samples flowing around the fiber. In these conditions, a simple mathematical model allows quantification without the need of calibration curves. This work describes two portable dynamic air sampling (PDAS) devices designed for application of this approach to nonequilibrium SPME sampling and determination of airborne volatile organic compounds (VOCs). The use of a PDAS device resulted in greater adsorbed VOC mass compared to the conventional SPME extraction in static air for qualitative screening of live plant aromas and contaminants in indoor air. For all studied air samples, an increase in the number of detected compounds and sensitivity was also observed. Quantification of aromatic VOCs in indoor air was also carried out using this approach and the PDAS/SPME device. Measured VOC concentrations were in low parts-per-billion by volume range using only 30-s SPME fiber exposure and were comparable to those obtained with a standard NIOSH method 1501. The use of PDAS/SPME devices reduced the total air sampling and analysis time by several orders of magnitude compared to the NIOSH 1501 method.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".