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Record W4362447354 · doi:10.2109/jcersj2.22143

Selecting an appropriate binder to prepare alumina granules via spray freeze granulation drying

2023· article· en· W4362447354 on OpenAlexaff
Naoki Kondo, Akihiro Shimamura, Mikinori Hotta, Junichi Tatami, Shinya Kawaguchi

Bibliographic record

VenueJournal of the Ceramic Society of Japan · 2023
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsMD Precision (Canada)
FundersMinistry of Economy, Trade and Industry
KeywordsGranulationMaterials scienceComposite materialEmulsionSpray dryingEmulsion polymerizationPolymerGranule (geology)PolymerizationChemical engineering

Abstract

fetched live from OpenAlex

Spray freeze granulation drying (SFGD) is a granulation technique, involving the freezing of sprayed droplets, followed by freeze drying. This technique affords soft granules, which is advantageous for producing sintered bodies with few defects. However, non-spherical and/or hard granules are sometimes formed by SFGD, and the sintered body fabricated from such granules shows reduced density and strength. The formation of such non-spherical and/or hard granules is expected to be avoided by selecting an appropriate binder. Therefore, in this study, four types of binders (water-soluble acrylic polymer, acrylic emulsion, and poly vinyl alcohol with low and high degree of polymerization) were tested to produce alumina granules via SFGD. The shapes and properties of the granules, as well as densities and strength of the sintered bodies, were evaluated. By choosing a suitable binder, the formation of non-spherical granules can be significantly reduced, and soft granules are obtained. These leads to a sintered body with high density and high strength. The acrylic emulsion binder was found to be the best performing binder among the four binders.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.231
Teacher spread0.219 · 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 designSimulation or modeling
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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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