Gaseous and Particle‐bound VOC Products of Combustion Extracted by Needle Trap Samplers
Bibliographic record
Abstract
Abstract Gaseous benzene, toluene, ethylbenzene and o‐xylene (BTEX) were extracted by using the divinylbenzene (DVB) particles (mesh sizes 60–80, 80–100 and 100–120) as sorbents packed in passive needle trap samplers (NTS). This study performed feasibility tests of these self‐designed DVB‐NTS as diffusive time‐weighted average (TWA) samplers and compared extraction efficiency with that of 100 mm polydimethylsiloxane‐solid phase microextration (PDMS‐SPME) fiber for sampling gaseous and particle‐bound volatile organic compounds (VOCs) from burning mosquito coils. Experimental results indicated that extraction rate of NTS is a reliable index in extracting VOCs. Additionally, comparisons of the NTS in extracting BTEX mass showed the NTS packed with the smallest diameters of adsorbent particles (100–120 mesh DVB) were the most effective. The mass of gaseous BTEX extracted by 100 μm PDMS‐SPME fiber were substantially lower than that extracted by DVB‐NTS of all meshes for the 30‐min TWA sampling of burning mosquito coils, and NTS packed with 100–120 mesh DVB adsorbed BTEX 50–120 ng BTEX. Particles clogging inside the packed phase of NTS inhibited VOC extraction performance after 3–5 samplings of burning particles, especially NTS packed with small‐diameter adsorbents.
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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) | 0.001 | 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".