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Record W2237352249 · doi:10.1515/cttr-2015-0011

An Improved Theoretical Model of Cigarette Smoke Filtration across Mono-Segment Cellulose Acetate Filters

2015· article· en· W2237352249 on OpenAlexaboutno aff
Du Wen, Wen Jianhui, Xiaobin Zhang, Liu Hui-min

Bibliographic record

VenueBeiträge zur Tabakforschung international · 2015
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
Fundersnot available
KeywordsFiltration (mathematics)Cellulose acetateFiberImpactionCigarette smokeDiffusionParticle sizeAerosolFlow (mathematics)CelluloseMechanicsMaterials scienceChemistryChromatographyComposite materialMathematicsThermodynamicsPhysicsSurgeryStatisticsMedicine

Abstract

fetched live from OpenAlex

Summary An improved theoretical model was presented to predict the filtration efficiency of cigarette filters. Filtration equations of single fibers considering the interference of neighboring fibers were applied in the model. Cellulose acetate fibers in cigarette filters were approximated as cylinders. The fiber size was adjusted by its size projected on the flow field. The solid fraction of fibers in cigarette filters was recalculated using the size of the virtual cylinders. The varying flow velocity during smoking was taken into account when calculating the filtration efficiency. The effective hydrodynamic particle diameter of cigarette smoke was estimated to be 0.44 μm by the difference of filtration efficiencies under ISO and Health Canada Intense (HCI) smoking regimes. Filtration contributions due to diffusion, interception and inertial impaction were 62%, 32% and 6%, respectively, at a flow velocity of 0.38 m/s for particles of 0.44 μm diameter. The effect of inertial impaction was insignificant but not negligible under ISO smoking regime. The measured and predicted efficiencies of two cigarette samples were compared and satisfactory agreement was obtained. [Beitr. Tabakforsch. Int. 26 (2015) 232-240]

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score1.000

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 head0.023
GPT teacher head0.274
Teacher spread0.251 · 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.

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

Citations14
Published2015
Admission routes1
Has abstractyes

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