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Record W2316580097 · doi:10.1021/cg101536q

Crystallization, Polymorphism, and Binary Phase Behavior of Model Enantiopure and Racemic 1,3-Diacylglycerols

2011· article· en· W2316580097 on OpenAlexaff
R. John Craven, Robert W. Lencki

Bibliographic record

VenueCrystal Growth & Design · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEnantiopure drugCrystallizationRacemic mixtureEnantiomerChemistryDifferential scanning calorimetryPolymorphism (computer science)Phase (matter)Eutectic systemOrganic chemistryThermodynamicsEnantioselective synthesis

Abstract

fetched live from OpenAlex

1,3-Diacylglycerols (1,3-DAG) are components in many natural, commercial, and food systems. These compounds are always asymmetric, and diacid forms are chiral. To understand what effect this has on their crystallization behavior, model enantiopure (1-decanoyl-3-palmitoyl- sn -glycerol) and racemic (1,3-decanoyl-palmitoyl- rac -glycerol) 1,3-DAG were prepared and characterized. In addition, binary phase diagrams were prepared to investigate their phase behavior and the racemate’s crystalline tendency. The major finding for this work is eutectic phase behavior was seen for blends of opposite enantiomers indicating racemic mixtures form conglomerates (mechanical mixtures of enantiopure crystals) in the solid phase. Differential scanning calorimetry melting curves of the racemic mixture display marked polymorphism, whereas, the pure enantiomer did not. This can be understood from a structural perspective since chain-end matching and hydrogen-bond optimization (via orientation of glycerol) are simultaneous for enantiopure, but are multistage for racemic DAG. Thus, there are critical differences between the crystallization behavior of enantiopure and racemic 1,3-DAG, and future physical analysis of these compounds should reflect this finding.

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 score0.036
Threshold uncertainty score0.299

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 head0.049
GPT teacher head0.231
Teacher spread0.182 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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