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Record W2146128985 · doi:10.1109/tia.2005.853372

Induction Charging of Granular Materials in an Electric Field

2005· article· en· W2146128985 on OpenAlexaff
Yuanyi Wu, G.S.P. Castle, I.I. Inculet

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsElectric fieldParticle (ecology)Materials scienceElectrical resistivity and conductivityCharge densityElectric chargeField strengthFaraday cageRange (aeronautics)Surface chargeParticle sizeCharge (physics)Analytical Chemistry (journal)Condensed matter physicsComposite materialPhysicsMagnetic fieldChemistry

Abstract

fetched live from OpenAlex

It is well known that particles can be freely levitated in an electric field due to the charge induced on the particles by the external field. The charge depends upon the electric field strength and particle properties. This paper studies some of these factors to investigate the fundamentals of induction charging for granular materials. An experimental apparatus was set up to collect the levitated particles in a filter contained in a Faraday pail and the charge-to-mass (Q/M) ratio was obtained based on the charge and mass measurements for the samples in the filter. Furthermore, the particle size distribution was measured and analyzed by laser diffraction and microscopy and the surface mean diameter (D/sub s/) and volume mean diameter (D/sub v/) were obtained. In these experiments irregular shaped Al/sub 2/O/sub 3/ particles and spherical glass beads with a size range of 42-390 /spl mu/m were used and tested at different electric field strengths. By combining the results of Q/M, D/sub s/ and D/sub v/, the charge per particle was calculated and the results compared with theoretical values. It was confirmed that the particle charge is dependent upon the electric field strength and the particle properties of size, shape, density, resistivity, and adhesive force.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.267
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations13
Published2005
Admission routes1
Has abstractyes

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