Extraction of Gaseous VOCs Using Passive Needle Trap Samplers
Bibliographic record
Abstract
Gaseous toluene, ethylbenzene and o-xylene (TEX) were extracted by using the divinylbenzene (DVB) particle of 60–80 mesh as a sorbent packed in series of needle trap samplers (NTS). The feasibility of using this self-designed DVB-NTS as a diffusive time-weighted average (TWA) sampler for occupational hygiene applications was evaluated by examining extended sampling periods of 4–12 hr. Additionally, the NTS was compared in terms of extraction efficiency by simultaneously using the 75 μm Carboxen/polydimethylsiloxane-solid phase microextration (Carboxen/PDMS-SPME) fiber for sampling TEX. Experimental results indicated that, regardless of static (in a bulb) or dynamic samplings (in a flowing gas stream), the packed DVB-NTS achieved higher TEX extraction rates (ng VOCs/min) than those of 75 μm Carboxen/PDMS-SPME fiber. The decreasing rates of extracting TEX for SPME fiber were 24–34% and 28–36% as NTS performed during the sampling periods of 240 and 480 min, respectively. Typically, the maximum VOC adsorption capacities per mg DVB were measured as 5.692 μg toluene, 7.669 μg ethylbenzene and 5.199 μg o-xylene. During the sampling period of 12 hr, the DVB-NTS extracted 1600–1800 ng of individual TEX components from a continuously flowing air stream, in which the gas flow rate equals 200 mL/min. We recommend the badgelike or penlike NTS as an alternative passive sampler to the legal active sampling method 1501, National Institute of Occupational Safety and Health (NIOSH), owing to its small size and high extraction capacity of TEX.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".