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Record W3024262064 · doi:10.1002/app.49387

Effect of TPU hard segment content on the rheological and mechanical properties of PLA/TPU blends

2020· article· en· W3024262064 on OpenAlexaff
Mohammadreza Nofar, Mojtaba Mohammadi, Pierre J. Carreau

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsPolytechnique Montréal
FundersIstanbul Teknik Üniversitesi
KeywordsThermoplastic polyurethaneMaterials scienceCrystallinityRheologyComposite materialPlastics extrusionCrystallizationPolymer blendIzod impact strength testPhase (matter)Morphology (biology)Amorphous solidChemical engineeringPolymerElastomerCopolymerUltimate tensile strengthChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Blends of an amorphous polylactide (PLA) with three different thermoplastic polyurethane (TPU) grades having various hard segment (HS) contents are prepared at the blending ratio of 85/15 wt% through a twin‐screw extruder (TSE) at processing temperatures of 150 and 190°C. Blends of a semicrystalline PLA with 15 wt% of the noted TPU grades are also processed in the TSE at 190°C to investigate the matrix crystallization effect on the morphology and property enhancements. The rheological experiments reveal that the increase in TPU HS content significantly increases the phase compatibility between PLA and TPU as also suggested by the finer morphology of the TPU phase, although the use of lower HS TPUs is more favorable to enhance the ductility and impact properties of the blends.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.056
GPT teacher head0.227
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), 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

Citations83
Published2020
Admission routes1
Has abstractyes

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