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Record W2136226572 · doi:10.1002/aic.14495

High performance materials based on a self‐assembled multiple‐percolated ternary blend

2014· article· en· W2136226572 on OpenAlexafffund
Sepehr Ravati, Christine Beaulieu, Ali M. Zolali, Basil D. Favis

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrystallinityTernary operationMaterials scienceDifferential scanning calorimetryPolylactic acidPolybutylene succinateTernary numeral systemAdipateChemical engineeringModulusPolybutylene terephthalateComposite materialPolymerPolyesterThermodynamics

Abstract

fetched live from OpenAlex

In this study, it will be shown that morphologically tailored tricontinuous ternary blends, comprising polybutylene succinate (PBS), polylactic acid (PLA), and poly (butylene adipate‐co‐terephthalate)(PBAT), can generate new materials with excellent properties. Detailed morphological analysis is used to establish that all three phases in the ternary 33%PBS/33%PLA/33%PBAT blend morphology are highly continuous with a phase structure dominated by complete wetting dynamics. PBS is shown to situate itself between PLA and PBAT. This melt processed, self‐assembled, multiple percolated, blend possesses a high elongation at break (567%), high Young's modulus (1130 MPa), high impact strength (271 J/m), and a storage modulus about 50% higher than pure PBS at room temperature. None of the neat materials demonstrate this combination of high properties and the synergy derives from the tricontinuous structure of the system. The ternary nature of the blend allows for a modulation of the crystallinity behavior as examined by differential scanning calorimeter and X‐ray Diffraction. © 2014 American Institute of Chemical Engineers AIChE J , 60: 3005–3012, 2014

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.203
Teacher spread0.190 · 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.

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

Citations35
Published2014
Admission routes2
Has abstractyes

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