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Record W2135121956 · doi:10.1002/app.23185

Influence of the porous morphology on the <i>in vitro</i> degradation and mechanical properties of poly(<scp>L</scp>‐lactide) disks

2006· article· en· W2135121956 on OpenAlexaff
Pierre Sarazin, Nick Virgilio, Basil D. Favis

Bibliographic record

VenueJournal of Applied Polymer Science · 2006
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsPorosityMaterials sciencePorous mediumCrystallinityComposite materialVoid (composites)LactideDegradation (telecommunications)Chemical engineeringPolymerCopolymer

Abstract

fetched live from OpenAlex

Abstract Poly( L ‐lactide) (PLLA) materials having an interconnected porosity are proposed as an alternative to nonporous biomaterials. Such materials allow for the potential of modulating the degradation behavior and the mechanical properties. In this article, the preparation of porous PLLA disks or cylinders with 50 and 65% void volume is presented. It is demonstrated that both a symmetric and asymmetric porosity can be generated within the disk itself. In addition, open‐ and closed‐cell structures can also be prepared. The accelerated in vitro degradation on symmetric open‐cell porous PLLA disks and on the nonporous control indicate a similar behavior in terms of melting temperature and inherent viscosity of the remaining pieces of the specimens, but the crystallinity and the mass of the remaining fragments are much smaller for the porous specimens. The mechanical properties under compression are determined for open and closed‐cell porous cylinders, porous tubes, and for the nonporous PLLA. The results highlight the excellent mechanical integrity of the prepared porous structures and demonstrate that such materials could have potential for use as biomedical implants. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 1039–1047, 2006

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)0.002
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 score0.008
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.015
GPT teacher head0.205
Teacher spread0.191 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

Explore more

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