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Record W2327714453 · doi:10.1166/jnn.2009.1363

Self-Assembled Nanostructures of Oleic Acid and Their Capacity for Encapsulation and Controlled Delivery of Nutrients

2009· article· en· W2327714453 on OpenAlexaff
Sinoj Abraham, Suresh S. Narine

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Chemistry and Fat Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOleic acidMaterials scienceMicelleNanostructureVesicleChemical engineeringDynamic light scatteringTransmission electron microscopyNanotechnologySupramolecular chemistryOrganic chemistryNanoparticleMoleculeChemistryBiochemistry

Abstract

fetched live from OpenAlex

A simple preparation method for a nutrient delivery system is presented using nanostructures of oleic acid embedded in canola oil by a two step synthesis procedure. Oleic acid nanostructures in the form of micelles and vesicles were initially prepared and their ability to encapsulate vitamin-C, used as a model nutrient, was evaluated. Dynamic Light Scattering (DLS) analysis and Transmission Electron Microscopy (TEM) confirmed the formation of micelles and vesicles with diameters of 150 nm and 540 nm respectively. Supramolecular structural transformations were observed at specific temperatures, and coincided with the release of a considerable amount of encapsulants followed by a controlled and time bounded release. UV-Vis spectroscopy and various other thermochemical analytical techniques were employed to illustrate the structural transformations and specific environmental stimuli-responsive release of encapsulated materials. The nutrition-enriched nanostructures were successfully transferred to canola oil and the physico-chemical properties evaluated.

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 score0.024
Threshold uncertainty score0.149

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 head0.197
Teacher spread0.189 · 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

Citations10
Published2009
Admission routes1
Has abstractyes

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