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Record W4252823114 · doi:10.1515/ipp-2020-350508

Preparation, Characterization and Processing of PCL/PHO Blends by 3D Bioplotting

2020· article· en· W4252823114 on OpenAlexaff
Salim Gopi, Bruce A. Ramsay, J. A. Ramsay, Marianna Kontopoulou

Bibliographic record

VenueInternational Polymer Processing · 2020
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials scienceCrystallinityCrystallizationPolycaprolactoneCompoundingComposite materialViscosityChemical engineeringPolymer

Abstract

fetched live from OpenAlex

Abstract Blends of polycaprolactone (PCL) and poly(3-hydroxyoctanoate) P(3HO) were prepared by melt compounding. These immiscible blends exhibited droplet-matrix morphology at compositions up to 30 wt% P(3HO). Even though the addition of amorphous P(3HO) decreased the crystallinity of PCL, the crystallization temperature of the blends increased by 6 to 7 8C. Blends containing up to 30 wt% P(3HO) had higher crystallization rates, and lower crystallization half-times compared to neat PCL. The viscosity of PCL decreased upon addition of P(3HO), making the blends suitable for processing using a 3D bioplotter. Compositions with 10 to 30 wt% P(3HO) were ideal for processing, because of their improved crystallization kinetics, reduced stickiness and good flow properties. Estimation of the interfacial tension by fitting the Palierne model to the linear viscoelastic properties of the blends revealed good compatibility, which gave rise to synergistic effects in the thermal and mechanical properties. The fibres prepared by 3D bioplotting maintained droplet matrix morphology, with finer particle size than the original compounded material. In addition to favourable viscosity and thermal properties, the extruded fibres containing 30 wt% P(3HO) had comparable modulus to the neat PCL, while exhibiting good ductility. These blends may be suitable alternatives to PCL for biomedical applications, because they provide a range of crystallinities, crystallization rates and viscosities.

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)0.000
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 score0.181
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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