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Record W1973480888 · doi:10.1021/ie400521a

Facile Preparation of Soy Protein/Poly(vinyl alcohol) Blend Fibers with High Mechanical Performance by Wet-Spinning

2013· article· en· W1973480888 on OpenAlexaff
Dagang Liu, Changqing Zhu, Kai Peng, Yi Guo, Peter R. Chang, Xiaodong Cao

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsVinyl alcoholDifferential scanning calorimetrySoy proteinScanning electron microscopeMaterials scienceGlass transitionChemical engineeringSpinningThermal stabilityFormic acidComposite materialPolymer chemistryPolymerChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Using formic acid as a cosolvent and saturated sodium sulfate as a coagulation bath, soy protein/poly(vinyl alcohol) (PVA) blend fibers were prepared using wet-spinning approaches. The structure and mechanical, thermal, and water-uptake properties of the spun fibers were investigated. Morphological analysis with polarized optical microscopy (POM) and scanning electron microscopy (SEM) showed that spun PVA or blend fibers were composed of nanoparticles and exhibited a porous morphology. Blend fibers exhibited only one glass transition temperature in differential scanning calorimetry (DSC) thermograms due to high compatibility between the two components. The best mechanical strength and thermal stability were achieved when 70% PVA was composited with soy protein. This was thought to be due to the effects of cross-linking and hydrogen bonding between functional groups of soy protein and PVA hydroxyl groups.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.064
GPT teacher head0.316
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2013
Admission routes1
Has abstractyes

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