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Record W4388768487 · doi:10.1002/app.54867

Interactions between <scp>PLA</scp>, organo‐montmorillonite and plasticizer: Synergistic effect on the barrier and mechanical properties of <scp>PLA</scp> nanocomposites blown films

2023· article· en· W4388768487 on OpenAlexafffund
Salima Adrar, Abdellah Ajji

Bibliographic record

VenueJournal of Applied Polymer Science · 2023
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlasticizerMaterials scienceMontmorilloniteNanocompositePolylactic acidCompatibilizationCrystallinityChemical engineeringComposite materialPolymerPolymer chemistryPolymer blendCopolymer

Abstract

fetched live from OpenAlex

Abstract In this study, the impact of incorporating a plasticizer on the compatibility between organo‐montmorillonite (OMMt) and polylactic acid (PLA) is investigated, and the resulting barrier and mechanical properties are reported. Four polymers were chosen as plasticizers to prepare the PLA nanocomposite blown films: poly(ethylene glycol), poly(ethylene oxide), polycaprolactone (PCL), and random ethylene‐methyl acrylate‐glycidyl methacrylate terpolymer. Firstly, 5 wt% of each plasticizer and 3 wt% of OMMt (Dellite® D43B) were mixed simultaneously with PLA in a twin‐screw extruder and then introduced into the hopper of a single screw extruder to produce D43B‐PLA/plasticizer nanocomposite films. The compatibilization effect was examined based on microstructure observations and thermodynamic predictions. Crystallinity was evaluated using DSC and XRD measurements. The results obtained for permeability and mechanical testing showed that the improvement of barrier and mechanical properties depends directly on the degree of compatibility between plasticizer, OMMt, and PLA. Indeed, the interfacial properties, XRD diffraction, and TEM images showed that a synergistic effect can result from high interfacial interactions between different compounds.

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)0.001
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 score0.005
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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

Citations25
Published2023
Admission routes2
Has abstractyes

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