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Record W2124349085 · doi:10.1109/ias.2000.881928

Bipolar charging in polydisperse polymer powders in industrial processes

2002· article· en· W2124349085 on OpenAlexaff
Hongjian Zhao, G.S.P. Castle, I.I. Inculet, A.G. Bailey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsWestern University
Fundersnot available
KeywordsFaraday cupCharge (physics)Faraday cageMaterials sciencePolymerSpace chargeParticle (ecology)Volume (thermodynamics)Work (physics)Charge densityParticle-size distributionMass distributionAnalytical Chemistry (journal)Particle sizeComposite materialMagnetic fieldChemistryThermodynamicsPhysicsOpticsChromatographyPhysical chemistryBeam (structure)Nuclear physics

Abstract

fetched live from OpenAlex

The purpose of this paper is to review the previous published work and to describe some results showing bipolar charging using polymer powders in two common industrial processes: fluidized beds and pneumatic transport. A new measurement system is described for measuring the bipolar charge distribution. This consists of a vertical array of seven Faraday pail sensors, which can selectively detect different charge components based upon particle size (gravity segregation) and charge (space charge repulsion). For the experiments reported here the charge and mass values were measured for each sensor allowing the calculation of charge to mass ratio (Q/M). In addition, size distribution and surface analyses were carried out for representative samples of the powder components. Data are presented for several types of polymer powders (volume mean diameter <100 /spl mu/m), with and without extraparticulate additives. The results, in all but one of the cases reported, show that even though the net charge may be positive or negative the fine particles show a negative charge and the coarse particles positive. These results are compared under several possible hypotheses.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.020
GPT teacher head0.188
Teacher spread0.169 · 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 designObservational
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

Citations28
Published2002
Admission routes1
Has abstractyes

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