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Record W2560737610 · doi:10.1002/app.44677

Crystallinity and mechanical properties of polylactide/ether‐amide copolymer blends

2016· article· en· W2560737610 on OpenAlexaff
Thibault Jauzein, Michel A. Huneault, Marie‐Claude Heuzey

Bibliographic record

VenueJournal of Applied Polymer Science · 2016
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversité de SherbrookePolytechnique Montréal
Fundersnot available
KeywordsCrystallinityCopolymerMaterials sciencePolymer scienceEtherPolymer chemistryComposite materialPolymer blendChemical engineeringPolymerOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

ABSTRACT The present study focuses on the improvement of impact properties and particularly on the interaction between crystallinity development and mechanical properties of impact modified polylactide (PLA). The PLA was toughened by the addition of a random linear ether‐amide copolymer (PEBAX 3533™). A random copolymer of ethylene, methyl‐acrylate, and glycidyl‐methacrylate (LOTADER AX8900™) was also used to reactively compatibilize the ether‐amide copolymer with the PLA matrix. Melt rheology of the blends was investigated in small amplitude oscillatory shear and showed that the impact modifier could significantly influence the viscoelastic response of the material. The Izod impact resistance and tensile properties were measured using standard testing protocols. The blend morphology was also examined using scanning electron microscopy on cryofractured and microtomed surfaces, while the crystalline morphology was assessed by optical microscopy. A sub‐micron dispersion of the impact modifier was achieved in the presence of the reactive compatibilizer. Significantly improved impact strength was found with 10 wt % impact modifier. High crystallinity samples showed the highest impact strength with values reaching 68 kJ/m 2 , hence a 20‐fold improvement with respect to the neat PLA. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 44677.

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)0.002
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 score0.047
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.225
Teacher spread0.202 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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