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Record W2153698307 · doi:10.1177/0040517507078041

Aerosol Filtration by Fibrous Filters: A Statistical Mechanics Approach

2007· article· en· W2153698307 on OpenAlexaff
Wen Zhong, Ning Pan

Bibliographic record

VenueTextile Research Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAerosolFiltration (mathematics)FiberMonte Carlo methodStatistical mechanicsIsotropyParticle (ecology)MechanicsProcess (computing)Statistical physicsParticle filterIsing modelMaterials scienceBinary numberFilter (signal processing)Biological systemComputer scienceComposite materialPhysicsMathematicsMeteorologyOpticsStatistics

Abstract

fetched live from OpenAlex

A statistical mechanics approach, namely the Ising model combined with Monte Carlo simulation, was employed in studying the process of aerosol filtration through fibrous filters. This process was modeled as consisting of numerous cells' state exchanges driven by the difference of the system energy after and before a particle moved from one cell to the other and/or deposited on a fiber cell. With the use of a simpler binary algorithm, this approach was capable of realistically simulating the complicated mechanisms involved in the filtration process. Simulations were carried out for the behaviors of aerosol particles of different sizes interacting with isotropic fiber filters of various volume fractions. Simulation results were in good agreement with reported experimental data, indicating an encouraging prospect for the method to be applied in this area.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2007
Admission routes1
Has abstractyes

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Same venueTextile Research JournalSame topicAerosol Filtration and Electrostatic PrecipitationFrench-language works237,207