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Record W2094786277 · doi:10.1021/am3004522

Fully Biodegradable and Biorenewable Ternary Blends from Polylactide, Poly(3-hydroxybutyrate-co-hydroxyvalerate) and Poly(butylene succinate) with Balanced Properties

2012· article· en· W2094786277 on OpenAlexaff
Kunyu Zhang, Amar K. Mohanty, Manju Misra

Bibliographic record

VenueACS Applied Materials & Interfaces · 2012
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMaterials scienceMiscibilityPolybutylene succinatePolymer blendToughnessCrystallizationComposite materialBiodegradable polymerTernary operationChemical engineeringMelt flow indexPolymerCopolymer

Abstract

fetched live from OpenAlex

A ternary blend of entirely biodegradable polymers, namely polylactide (PLA), poly(3-hydroxybutyrate-co-hydroxyvalerate) (PHBV), and poly(butylene succinate) (PBS), was first melt-compounded in an effort to prepare novel fully biodegradable materials with an excellent balance of properties. The miscibility, morphology, thermal behavior, mechanical properties, and thermal resistance of the blends were investigated. DMA analysis revealed that PHBV and PLA showed some limited miscibility with each other, but PBS is immiscible with PLA or PHBV. Minor phase-separated structure was observed from SEM for all the blends composition except PHBV/PLA/PBS 60/30/10 blend, which formed a typical mixture of core-shell morphology. The morphologies were verified by analysis of the spreading coefficients. Excellent stiffness-toughness balance was achieved by ternary blends of PLA, PHBV, and PBS. Significant enhancement of the toughness and flexibility of PLA was achieved by the incorporation of PBS and PHBV without sacrificing the strength apparently. Both the stiffness and toughness were improved for PHBV in the ternary blends with PHBV as matrix. The crystallization of the PLA and PBS were enhanced by presence of PHBV in the blends, while the crystallization of PHBV was confined by PLA and PBS phases. Moreover, the thermal resistances and melt flow properties of the materials were also studied by analysis of the heat deflection temperature (HDT) and melt flow index (MFI) value in the work.

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 score0.000
Threshold uncertainty score0.002

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)0.0000.000

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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

Citations324
Published2012
Admission routes1
Has abstractyes

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