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Record W2066736945 · doi:10.1252/jcej.34.634

New Dispersing Turbines for the Preparation of Concentrated Suspensions.

2001· article· en· W2066736945 on OpenAlexaff
Olivier Furling, Philippe A. Tanguy, PIERRE HENRIC, DOMINIQUE DENOEL, L. Choplin

Bibliographic record

VenueJOURNAL OF CHEMICAL ENGINEERING OF JAPAN · 2001
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSlurryDisperserDispersion (optics)TurbineImpellerPower consumptionSuspension (topology)DispersantRotational speedMaterials scienceAgitatorEnvironmental sciencePower (physics)EngineeringMechanical engineeringComposite materialMathematicsPhysics

Abstract

fetched live from OpenAlex

The performance of two new dispersing tools (Deflo and Sevin turbines) were tested for the preparation of highly pigmented solids slurries. Their power consumption and dispersing efficiency are compared against the performance of the classical Cowles sawtooth turbine. The experiments are carried out with kaolin clays at solid concentration up to 72 wt.%. The influence of powder feeding rate on the slurry preparation is also investigated. Although the classical Cowles disperser is able to promote good slurry dispersion, the power consumption is significant due to the high rotational speed required to maintain a sufficient circulation in the tank. The Deflo and Sevin turbines give the same results for the dispersion quality, but the power consumption is greater with the Deflo turbine, which makes the Sevin impeller a very promising technology for high solids slurry preparation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.225
Teacher spread0.216 · 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 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

Citations8
Published2001
Admission routes1
Has abstractyes

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