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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 OpenAlex
Olivier Furling, Philippe A. Tanguy, PIERRE HENRIC, DOMINIQUE DENOEL, L. Choplin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.107
Threshold uncertainty score0.282

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 communication0.0000.000
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 head0.008
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