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Record W2158072664 · doi:10.4209/aaqr.2012.02.0046

Development of a Novel Porous Membrane Denuder for SO2 Measurement

2012· article· en· W2158072664 on OpenAlexaff
Lin Shou, Alex Theodore, Chang‐Yu Wu, Yu-Mei Hsu, Brian K. Birky

Bibliographic record

VenueAerosol and Air Quality Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsAlberta Conservation Association
Fundersnot available
KeywordsPorositySulfur dioxideCoatingMembraneParticle sizeMaterials scienceAnalytical Chemistry (journal)ChemistryComposite materialChromatographyInorganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.376
GPT teacher head0.419
Teacher spread0.043 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2012
Admission routes1
Has abstractyes

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