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Record W2314879851 · doi:10.1093/annhyg/mev005

Particle Loading Time and Humidity Effects on the Efficiency of an N95 Filtering Facepiece Respirator Model under Constant and Inhalation Cyclic Flows

2015· article· en· W2314879851 on OpenAlexaff
Alireza Mahdavi, Fariborz Haghighat, Ali Bahloul, Clothilde Brochot, Claude Ostiguy

Bibliographic record

VenueThe Annals of Occupational Hygiene · 2015
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsUniversité de MontréalConcordia UniversityNatural Sciences and Engineering Research Council of CanadaInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsRespiratorPenetration (warfare)InhalationParticle sizeRelative humidityMaterials scienceTime constantVolumetric flow rateHumidityInhalation exposureVolume (thermodynamics)Particle (ecology)ChemistryComposite materialMechanicsMeteorologyThermodynamicsAnesthesiaMedicinePhysics

Abstract

fetched live from OpenAlex

It is necessary to investigate the efficiencies of filtering facepiece respirators (FFRs) exposed to ultrafine particles (UFPs) for long periods of time, since the particle loading time may potentially affect the efficiency of FFRs. This article aims to investigate the filtration efficiency for a model of electrostatic N95 FFRs with constant and 'inhalation-only' cyclic flows, in terms of particle loading time effect, using different humidity conditions. Filters were exposed to generated polydisperse NaCl particles. Experiments were performed mimicking an 'inhalation-only' scenario with a cyclic flow of 85 l min(-1) as the minute volume [or 170 l min(-1) as mean inhalation flow (MIF)] and for two constant flows of 85 and 170 l min(-1), under three relative humidity (RH) levels of 10, 50, and 80%. Each test was performed for loading time periods of 6h and the particle penetration (10-205.4nm in electrical mobility diameter) was measured once every 2h. For a 10% RH, the penetration of smaller size particles (<80nm), including the most penetrating particle size (MPPS), decreased over time for both constant and cyclic flows. For 50 and 80% RH levels, the changes in penetration were typically observed in an opposite direction with less magnitude. The penetrations at MPPS increased with respect to loading time under constant flow conditions (85 and 170 l min(-1)): it did not substantially increase under cyclic flows. The comparison of the cyclic flow (85 l min(-1) as minute volume) and constant flow equal to the cyclic flow minute volume indicated that, for all conditions the penetration was significantly less for the constant flow than that of cyclic flow. The comparison between the cyclic (170 l min(-1) as MIF) and constant flow equal to cyclic flow MIF indicated that, for the initial stage of loading, the penetrations were almost equal, but they were different for the final stages of the loading time. For a 10% RH, the penetration of a wide range of sizes was observed to be higher with the cyclic flow (170 as MIF) than with the equivalent constant flow (170 l min(-1)). For 50 and 80% RH levels, the penetrations were usually greater with a constant flow (170 l min(-1)) than with a cyclic flow (170 l min(-1) as MIF). It is concluded that, for the tested electrostatic N95 filters, the change in penetration as a function of the loading time does not necessarily take place with the same rate under constant (MIF) and cyclic flow. Moreover, for all tested flow rates, the penetration is not only affected by the loading time but also by the RH level. Lower RH levels (10%) have decreasing penetration rates in terms of loading time, while higher RH levels (50 and 80%) have increasing penetration rates. Also, the loading of the filter is normally accompanied with a shift of MPPS towards larger sizes.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.101
GPT teacher head0.324
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations37
Published2015
Admission routes1
Has abstractyes

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