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Record W2315947294 · doi:10.1021/cg501221d

Spheroidal Fat Crystal Microstructures Formed with Confined Gap Shearing

2014· article· en· W2315947294 on OpenAlexafffund
Tu Tran, Supratim Ghosh, Dérick Rousseau

Bibliographic record

VenueCrystal Growth & Design · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of SaskatchewanToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShearing (physics)Shear rateMicrostructureShear (geology)RheologyCrystallizationMaterials scienceShear stressCanolaComposite materialOptical microscopeChemical engineeringCrystal structureChemistryCrystallographyOrganic chemistryScanning electron microscope

Abstract

fetched live from OpenAlex

Confined gap shear-cooling was used to structure a model solid fat/liquid oil system consisting of fully hydrogenated canola oil (FHCO) and canola oil (CO). Samples were cooled at various cooling rates (0.2–5.0 °C/min) and shear rates (0, 500, 1000, and 2000 s –1 ) and characterized via polarized light microscopy and rheology. In the absence of shear (0 s –1 ), FHCO crystallized into an aggregated network of spherulites. Upon shearing, HCO crystallized into distinct spheroids whose size and structure were dependent on the cooling and shear rates used as well as crystallization duration and temperature. Lower shear rates resulted in larger, more irregularly shaped spheroids, whereas with higher shear rates these were more numerous and smaller. The simultaneous action of cooling and shear was required for spheroid formation, with shear having a greater impact. A mechanism was proposed to identify the multiple steps involved in the formation of the spheroids. Overall, these results demonstrated that it is possible to tailor the aggregation behavior of individual fat crystals toward novel morphologies that may be used to control fat crystal network microstructure and rheology.

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 score0.035
Threshold uncertainty score0.556

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)0.0010.000

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 head0.017
GPT teacher head0.193
Teacher spread0.176 · 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

Citations19
Published2014
Admission routes2
Has abstractyes

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