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Record W1974960617 · doi:10.1002/jbm.b.31364

Compression properties of polyvinyl alcohol – bacterial cellulose nanocomposite

2009· article· en· W1974960617 on OpenAlexaff
Leonardo E. Millon, Christine J. Oates, Wankei Wan

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2009
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsNanocompositeMaterials sciencePolyvinyl alcoholComposite materialVinyl alcoholBacterial celluloseBiomaterialNanofiberCompressive strengthCartilageCellulosePolymerChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Despite the established use of total joint replacement for the treatment of advanced degeneration of articular cartilage, component loosening due to wear and osteolysis limits the lifespan of these joint prostheses. In the present study, nanocomposites consisting of poly(vinyl alcohol) (PVA) and bacterial cellulose (BC) nanofibers were investigated as possible improved cartilage replacement materials. Nanocomposites were synthesized by adding small amounts (<1%) of BC to PVA, and subjecting the mixture to thermal cycling. The mechanical properties of the resulting material were evaluated using unconfined compression testing. By the addition of BC nanofibers to the PVA matrix, a nanocomposite with a wide range of compressive mechanical properties control was obtained, with elastic modulus values similar to those reported for native articular cartilage. The nanocomposite also showed improved strain-rate dependence and adequate viscoelastic properties. The PVA-BC nanocomposite is therefore a promising biomaterial to be considered as a possible replacement material for localized articular cartilage injuries and other orthopedic applications such as intervertebral discs.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.335
Teacher spread0.283 · 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.

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

Citations98
Published2009
Admission routes1
Has abstractyes

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