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Record W4294775237 · doi:10.1016/j.carbpol.2022.120080

Changes to fine structure, size and mechanical modulus of phytoglycogen nanoparticles subjected to high-shear extrusion

2022· article· en· W4294775237 on OpenAlexafffund
Laura Román, Benjamin Baylis, Karl M. Klinger, Jondavid de Jong, John Dutcher, Mario M. Martínez

Bibliographic record

VenueCarbohydrate Polymers · 2022
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMirexus (Canada)University of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExtrusionAmylopectinMaterials scienceNanoparticleComposite materialShear (geology)ChemistryNanotechnologyAmyloseStarchOrganic chemistry

Abstract

fetched live from OpenAlex

This study aims to enhance the understanding of the structure of maize phytoglycogen nanoparticles, and the effect of shear scission on their architecture, radius, stiffness, and deformability. Compared to amylopectin, phytoglycogen had a lower A:B chain ratio, a lower number of chains per B chain, and a much higher number of Afingerprint chains. Phytoglycogen (Mw = 28.0 × 106 g/mol) was subjected to high-shear extrusion with varying Specific Mechanical Energies (SMEs) using different screw speeds, showing a maximum stable molecular weight Mw of ∼9.31 × 106 g/mol and a particle radius R reduction of 36 %, with a corresponding 20 % increase in the average mass density. Atomic force microscopy force spectroscopy revealed that nanoparticles extruded at the lowest SME (122 Wh/kg) exhibited a 20 % increase in Young's modulus. Higher SME values (up to 488 Wh/kg) resulted in an overall decrease in stiffness without further significant reductions in radius.

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 score0.013
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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

Citations23
Published2022
Admission routes2
Has abstractyes

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