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Record W2736710128 · doi:10.1007/s11746-017-3021-z

Crystallization Behavior of High Behenic Acid Stabilizers in Liquid Oil

2017· article· en· W2736710128 on OpenAlexafffund
Ga Yae Kim, Alejandro G. Marangoni

Bibliographic record

VenueJournal of the American Oil Chemists Society · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBehenic acidCrystallizationPeanut oilNucleationOleic acidSpherulite (polymer physics)Materials scienceChemistryVegetable oilFractionationChemical engineeringChromatographyOrganic chemistryFatty acidBiochemistry

Abstract

fetched live from OpenAlex

Abstract The crystallization behavior and structure of mixtures of a high behenic acid stabilizer (HBS) in peanut oil, high oleic safflower oil and sesame oil were studied in order to elucidate the mechanism behind liquid oil stabilization. Both the chemical composition of the oil and cooling rate influenced the crystallization behavior and structure of HBS. The critical gelation concentration of HBS ranged from 6.5% for peanut oil crystallized at 3 °C/min to 11% for sesame oil mixtures crystallized at 0.6 °C/min. The free energy of nucleation (Δ G ) was the highest for sesame oil (142 kJ/mol) followed by high oleic safflower oil (75.8 kJ/mol) and peanut oil (15.9 kJ/mol). The HBS peanut oil mixture displayed the highest storage modulus ( G ′) under both cooling rates studied. In general, HBS‐oil mixtures crystallized at a higher cooling rate exhibited high SFC values, lower crystallization temperatures and a predominance of the β′ polymorph, and they had a microstructure characterized by uniformly sized spherulites. In contrast, slow cooling rates led to higher critical gelation concentrations of HBS, lower SFC, fractionation of higher melting and lower melting fractions, a more stable polymorphic form β and a wide range of spherulite sizes.

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 score0.042
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.235
Teacher spread0.223 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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