Time-Weighted Average Passive Sampling with a Solid-Phase Microextraction Device
Bibliographic record
Abstract
A modified Solid-Phase Microextraction (SPME) device has been used as a passive sampler to determine the time-weighted average (TWA) concentration of volatile organic compounds (VOCs) in air. Unlike conventional sampling with SPME, in which the fiber is extended outside its needle housing, during TWA passive sampling, the fiber is retracted a known distance into its needle housing. The SPME passive sampler collects the VOCs by the mechanism of molecular diffusion and sorption on to a coated fiber as collection medium. This process has been shown to be described by Fick's first law of diffusion, whereby determination of the amounts of analytes accumulated over time enable measurement of the TWA concentration to which the sampler was exposed. A series of fibers, 100-microm poly(dimethylsiloxane), 65-microm poly(dimethylsiloxane)/divinylbenzene, and 75-microm Carboxen/poly(dimethylsiloxane), were tested for their "zero sink", face velocity, and response time behavior. Of the fibers tested, that coated with 75-microm Carboxen/poly(dimethylsiloxane) was found to be an excellent passive sampler for VOCs. TWA passive sampling with a SPME device was shown to be almost independent of face velocity and to be more tolerant of high and low analyte concentrations and long and short sampling times, because of the ease with which the diffusion path length could be changed. It was found that environmental conditions, e.g., temperature, pressure, relative humidity, and ozone, have little or no effect on sampling. The 75-microm Carboxen/poly(dimethylsiloxane) fiber can retain VOCs for up to two weeks without significant loss. When the SPME device was tested in the field and the results were compared with those from National Institute of Occupational Health and Safety method 1501, good agreement was obtained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".