Development of a Novel Porous Membrane Denuder for SO2 Measurement
Bibliographic record
Abstract
Conventional denuders made of glass or metal are too heavy and bulky to be used in personal sampling systems. In this study, a portable Porous Membrane Denuder (PMD) was developed for personal sampling. The PMD utilizes the porosity of the membrane material and a configuration of multiple parallel flow channels to reduce the size and weight of the device, while increasing the gas collection efficiency. Four types of PMDs (PMD I, II, III and IV) with increasing numbers of channels and smaller channel openings were constructed and tested. Using 10% sodium carbonate coating and a feed concentration of 1 ppm, PMD Ia’s collection efficiency for sulfur dioxide over 8 hours was higher than 99.9%. For a feed concentration of 10 ppm, the 5-hr time-weighted-average collection efficiency for sulfur dioxide was 73.1%, 82.8%, 90.9%, 97.2% for PMD Ib, II, III and IV, respectively, compared with 96.6% of the Glass Honeycomb Denuder (GHD), which has similar structure to PMD IV. However, the weight of PMD IV is only one-tenth of that of the GHD. It is clear that with a similar physical structure, PMDs have similar capacities to traditional Glass Honeycomb Denuders, yet are much lighter and less expensive. This study demonstrates the great potential of this new type of denuder for many applications in the field of environmental and industrial hygiene monitoring. Particle loss fractions of the four types of PMDs in the size range of 1 to 10 μm were also measured and were 2.9%, 5.2%, 5.7% and 7.3%, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".