Preparation of a Particle-Loaded Membrane for Trace Gas Sampling
Bibliographic record
Abstract
A divinylbenzene (DVB) particle-loaded membrane with high extraction capacity was prepared using the bar coating method. The prepared membrane was evaluated in terms of morphology, effect of particle ratio, and membrane size on extraction efficiency, as well as linear calibration curve and limit of detection. The SEM (scanning electron microscope) images showed that the DVB particles were uniformly distributed in the PDMS base, ensuring the repeatability of the membranes. The extraction amount was quantified by gas chromatography-mass spectrometry coupled with a thermal desorption unit. Results showed that the extraction efficiency of the prepared membrane increased about 2 orders of magnitude for benzene sampling as the particle ratio increased from 0% to 30%, and the extraction amount was linearly proportional to the size of the membrane. A comparison with a pure PDMS membrane and DVB/PDMS fiber for outdoor air sampling showed that the extraction efficiency of the DVB/PDMS membrane was significantly enhanced, especially for volatile and polar compounds. The limit of detection was about 0.03 ng/mL for benzene in air, and the linear dynamic range extended to 100 ng/mL. An equilibrium calibration method was proposed for low-level air pollutant sampling using this high capacity membrane, and a displacement effect was not observed. To demonstrate the power of the technique, the developed approach was applied to monitor both spot and time weighted average (TWA) concentrations of benzene in outdoor air. A high spot concentration of benzene was observed in morning and afternoon rush hours, with TWA concentrations of 10.7 ng/L measured over the 11-h monitoring period.
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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) | 0.001 | 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 | 0.000 | 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".