Development of a thoracic personal sampler system for co-sampling of sulfuric acid mist and sulfur dioxide gas
Bibliographic record
Abstract
A novel personal sampler was designed to measure inorganic acid mists and gases for determining human exposure levels to these acids in workplaces. This sampler consists of (1) a parallel impactor for classifying aerosol by size following the ISO/CEN/ACGIH defined human thoracic fraction, (2) a cellulose filter to collect the residual acid mist but allowing penetration of sulfur dioxide gas, and (3) an accordion-shaped porous membrane denuder (aPMD) for adsorbing the penetrating sulfur dioxide gas. Acid-resistant PTFE was chosen as the housing material to minimize sampling interference.To test the performance of the parallel impactor, monodisperse aerosol was created by a vibrating orifice aerosol generator. The results showed that the penetration curve of the impactor run at 2 LPM flow rate agreed well with the defined thoracic fraction. Almost all sampling biases were within 10% for particle size distributions with MMAD between 1–25 µm and GSD between 1.75–4, which meets the criteria of the EN 13205 standard. To evaluate the performance of the aPMDs, sulfur dioxide gas was sourced directly from a cylinder. The aPMDs maintained a gas collection efficiency greater than 95% for 4 hr when sampling 8.6 ppm of sulfur dioxide gas. While the aPMD had similar performance to the commonly adopted annular or honeycomb denuders made of glass, this shatterproof aPMD is only half of the volume and 1/25th the weight of the honeycomb denuder. Testing of the entire sampler with a mixture of sulfuric acid mist and sulfur dioxide gas showed the system could sample both with negligible interference. All the test results illustrate that the new sampler, which is flat, lightweight, and portable, is suitable for personal use and is capable of a more accurate assessment of human exposure to inorganic acid mist and SO2 gas.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".