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Performance of mechanical filters used in general ventilation against nanoparticles

2019· article· en· W2982014056 on OpenAlex
Clothilde Brochot, Ali Bahloul, Pooya Abdolghader, Fariborz Haghighat

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailConcordia University
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsMaterials scienceASHRAE 90.1HEPAPressure dropParticle sizeNanoparticleRangingPenetration (warfare)Filtration (mathematics)Composite materialAcousticsNanotechnologyDrop (telecommunication)Computer scienceMechanicsChemical engineeringMathematicsMechanical engineeringEngineeringPhysicsMeteorologyStatistics

Abstract

fetched live from OpenAlex

Abstract Filtration is a simple and effective way to capture particles of different sizes. According to ANSI/ASHRAE 52.2 standard, ventilation filters efficiency is tested for particles ranging from 0.3 to 10.0 μm. To our knowledge, performances of entire filters for nanoparticles are still very limited and particle size of 300 nm is commonly used as the Most Particle Penetration Size (for mechanical media). In order to evaluate the filter performance for nanoparticles, five type of filters (from MERV 8 to HEPA) were evaluated via two measurements: penetration and pressure drop. Results are consistent with previous experimental measurements on media and entire filters. These data show that the range of 150 to 500 nm is a better estimation of the MPPS, unlike the fixed diameter of 300 nm.

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.

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)0.000
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 score0.135
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.200
Teacher spread0.190 · 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