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Record W2999033537 · doi:10.1002/app.48940

Polycaprolactone blends for fracture fixation in low load‐bearing applications

2020· article· en· W2999033537 on OpenAlexaff
Antony Bou‐Francis, Marta Piercey, Omar Al‐Qatami, Gianfranco Mazzanti, Rabie Khattab, Amyl Ghanem

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials sciencePolycaprolactoneDifferential scanning calorimetryMiscibilityComposite materialAdhesivePolymer blendTernary operationChitosanThermoplasticPolymerChemical engineeringCopolymer

Abstract

fetched live from OpenAlex

ABSTRACT There is a need to replace surgical plates and screws in orthopedic surgery. Absorbable polymers are an alternative to metal where load bearing is of a less concern. Polycaprolactone (PCL) is biocompatible, yet it has low mechanical strength and its surface chemistry does not promote cell adhesion. The objective of this work was to create PCL adhesive blends with poly(glycolic) acid (PGA), thermoplastic starch (TPS), chitosan, and tricalcium phosphate (TCP) to be used as potential fracture fixation devices. The differential scanning calorimetry (DSC) data showed that the primary melting points ( T m 1 °C) of blends were often lower than PCL, with the exception of chitosan blends, which may indicate an improvement for surgical use. PCL/PGA blends showed secondary and tertiary melting points ( T m ) and enthalpies (Δ H m ) indicating poor miscibility of PGA in the blends. The binary PCL/TCP mixture has a higher enthalpy compared to the binary PCL/PGA blend, but the secondary melting temperature is lower in ternary mixtures. Ternary blends of PCL/PGA/TCP, however, retained the adhesive strength of the parent PCL adhesive while having an improvement in hydrophilicity. These blends are recommended for fracture fixation devices especially in low load‐bearing applications such as maxillofacial surgery, orthopedics, and neurosurgery. © 2020 Wiley Periodicals Inc. J. Appl. Polym. Sci. 2020 , 137 , 48940.

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 score0.157
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.243
Teacher spread0.224 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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