MétaCan
Menu
Back to cohort
Record W2023550550 · doi:10.1002/cjce.20345

Fluidization characteristics of printed circuit board plastic particles with different sizes

2010· article· en· W2023550550 on OpenAlexvenueno aff
Qingjie Guo, Yongzhuo Liu, Minghua Wang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsnot available
FundersProgram for New Century Excellent Talents in UniversityNational Natural Science Foundation of China
KeywordsFluidizationFluidized bedConsolidation (business)Materials scienceThermodynamicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract The experiments were carried out in a fluidized bed of 56 mm in diameter and 1 600 mm in height to determine the fluidization characteristics of four sizes of printed circuit board plastic (PCBP) particles. It indicates that the fluidization characteristics of PCBP particles depend on the average size and particle type. 123 µm PCBP particles (1#), belonging to Geldart A group with strong viscous force, whose fluidization behaviours was similar to those of Geldart C, was difficult to fluidize. Whereas, 275 µm (2#), 354 µm (3#), and 423 µm (4#) PCBP particles, belonging to Geldart B, were fluidized smoothly. The bed collapsing process is composed of three stages: the bubble escaping stage, the sedimentation stage, and the solid consolidation stage. The collapsing process of 1# PCBP particle lasts 6 s or long. 2#, 3#, and 4# PCBP particles, Geldart group B particles, collapse process consists of the bubble escaping stage and the solid consolidation stage. The minimum fluidization velocities from modified Ergun Equation were agreement with experimental data for 2#, 3#, and 4# PCBP particles.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.157
Teacher spread0.152 · 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 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

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicGranular flow and fluidized bedsFrench-language works237,207