Membrane Extraction with a Sorbent Interface for Headspace Monitoring of Aqueous Samples Using a Cap Sampling Device
Bibliographic record
Abstract
A cap-shaped device was employed for headspace sampling. This sampling device coupled to membrane extraction with a sorbent interface (MESI) is intended to perform on-site and on-line aqueous sample monitoring. A laboratory sampling testwas performed both at the water surface and under water, and it showed some advantages in underwater monitoring. A group of volatile organic compounds (VOCs), varying in PDMS/gas and gas/water distribution constants, benzene, toluene, ethylbenzene, o-xylene, and trichloroethylene (TCE), was used for the sampling study. Magnetic stirring of the sample and circulation of the headspace air with a microfan were used for the enhancement of mass transfer between sample matrix and membrane to obtain higher extraction rate and efficiency. The agitation approaches were investigated individually and compared. The results showed that simultaneous agitation in water and air could greatly improve the extraction efficiency. Good linearity and precision and low detection limits were obtained for water-surface monitoring. The study demonstrated that Cap-MESI is a useful tool for field headspace monitoring of volatile organic compounds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".