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Record W2124728119 · doi:10.1002/app.34994

Porous poly(lactic acid) and PLA‐nanocomposite structures

2011· article· en· W2124728119 on OpenAlexaff
Xia Liao, A. Victoria Nawaby, Hani E. Naguib

Bibliographic record

VenueJournal of Applied Polymer Science · 2011
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsNational Research Council CanadaUniversity of Toronto
Fundersnot available
KeywordsCrystallinityMaterials scienceNanocompositeNucleationCrystallizationCrystalliteDifferential scanning calorimetryUltimate tensile strengthComposite materialChemical engineeringScanning electron microscopeLactic acidChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This article presents foaming behaviors and cellular structures of poly(lactic acid) (PLA)‐clay nanocomposite and respective neat PLA foams using CO 2 as a foaming agent. The effects of nanoclay and foaming conditions on the foam morphology and mechanical properties of neat (PLA) and PLA‐clay nanocomposites have been investigated by temperature modulated differential scanning calorimeter (TMDSC), X‐ray diffraction, scanning electron microscopy, and tensile properties. CO 2 ‐induced crystallization in PLA and PLA‐clay nanocomposites and the crystallinity increased with an increase in saturation time. The present of clay acted as nucleation site to facilitate the crystallization process of PLA thus resulted in smaller crystallites and lower crystallinity. Unique skin‐core foam morphologies in PLA and PLA‐clay nanocomposites were obtained by controlling the crystallinity. Clay nanoparticles served as nucleation sites to generate foam morphology in PLA‐clay sample with high crystallinity. The introduction of the cellular structure improved the mechanical strength of the material and the specific strength of PLA foams were increased in the presence of a small amount of MMT. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

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 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 score0.107
Threshold uncertainty score0.534

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 studies0.0000.001
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 head0.022
GPT teacher head0.219
Teacher spread0.197 · 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

Citations28
Published2011
Admission routes1
Has abstractyes

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