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Record W4221018888 · doi:10.1002/app.52369

Moisture‐induced anti‐plasticization of polylactic acid: Experiments and modeling

2022· article· en· W4221018888 on OpenAlexafffund
Yu Chen, Tian Tang, Cagri Ayranci

Bibliographic record

VenueJournal of Applied Polymer Science · 2022
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsPolylactic acidPlasticizerMaterials scienceMoistureCreepComposite materialViscoelasticityDistilled waterRelative humidityExtrusionPolymerWater contentChemical engineeringChemistryGeotechnical engineeringChromatographyThermodynamics

Abstract

fetched live from OpenAlex

Abstract Polylactic acid (PLA) is a biodegradable polymer derived from bio‐renewable resources. The effect of diffused water molecules on the mechanical properties of PLA, before the onset of hydrolytic degradation, has been rarely studied. In this work, PLA fibers produced by melt‐extrusion were conditioned in chambers with different levels of relative humidity (36%, 75%, and 98%), as well as immersed in distilled water. Creep tests were conducted on dry and conditioned samples and creep compliances were extracted. With the increase of moisture content, the decrease in instantaneous elastic compliance, as well as the more flattened curves in the last stage of the creep tests, indicates the existence of water‐bridge‐anti‐plasticization effect. The modified Burgers‐Reimschuessel model developed in our previous work is found to be able to predict the effect of absorbed moisture on the viscoelasticity of PLA. The present work highlights the importance of considering the moisture's anti‐plasticization effect. The proposed methodology can be adopted to evaluate the effect of moisture on the viscoelasticity of PLA for different applications.

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 score0.011
Threshold uncertainty score0.405

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 science0.0010.000
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 head0.033
GPT teacher head0.255
Teacher spread0.222 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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