Air Sampling with Porous Solid-Phase Microextraction Fibers
Bibliographic record
Abstract
A new, rapid air sampling/sample preparation methodology was investigated using adsorptive solid-phase microextraction (SPME) fiber coatings and nonequilibrium conditions for volatile organic compounds (VOCs). This method is the fastest extraction technique for air sampling at typical airborne VOC concentrations. A theoretical model for the extraction was formulated based on the diffusion through the interface between the sampled (bulk) air and the SPME coating. Parameters that affect the extraction process including sampling time, air velocity, air temperature, and relative humidity were investigated with the porous (solid) PDMS/DVB and Carboxen/PDMS coatings. Very short sampling times from 5 s to 1 min were used to minimize the effects of competitive adsorption and to calibrate the extraction process in the initial linear extraction region. The predicted amounts of extracted mass compared well with the measured amounts of target VOCs. Findings presented in this study extend the existing fundamental knowledge related to sampling/sample preparation with SPME, thereby enabling the development of new sampling devices for the rapid sampling of air, headspace, water, and soil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".