Development and Application of a Needle Trap Device for Time-Weighted Average Diffusive Sampling
Bibliographic record
Abstract
A simple, cost-effective analysis combining solventless extraction, thermal desorption, and determination of volatile organic compounds (VOCs) was developed and validated. A needle trap device (NTD) packed with the sorbent Carboxen1000 was used as a time-weighted average (TWA) diffusive sampler to collect target compounds by molecular diffusion and adsorption to the packed sorbent. This process can be described with derivations of Fick's first law of diffusion, which expresses the relation between the TWA concentrations to which the passive sampler is exposed and the mass of analytes adsorbed to the packed sorbent in the sampler. The effects of experimental factors such as temperature, pressure, humidity, and face velocity were taken into account in applying diffusive sampling under nonideal conditions. This study demonstrates that NTD is effective for air analysis of benzene, toluene, ethylbenzene, and o-xylene (BTEX), due to the good adsorption/desorption quality of Carboxen 1000 and to the special geometric shape of the needle with a small cross section avoiding the need for calibration. Storage tests showed good storage stability for BTEX. Verification of the theoretical model showed good agreement between theoretical and experimental sampling rates. Method validation done against NIOSH method 1501, SPME, and NTD active sampling revealed good agreement between those methods. Automated NTD sample introduction to a gas chromatograph facilitates the use of this technology for industrial hygiene applications.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".