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Record W2082502405 · doi:10.1002/jbm.b.31040

Anisotropic polyvinyl alcohol—Bacterial cellulose nanocomposite for biomedical applications

2008· article· en· W2082502405 on OpenAlexaff
Leonardo E. Millon, Ganesh Guhados, Wankei Wan

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2008
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials sciencePolyvinyl alcoholNanocompositeBacterial celluloseComposite materialVinyl alcoholAnisotropyCelluloseUltimate tensile strengthPolymerChemical engineering

Abstract

fetched live from OpenAlex

Compliance mismatch between the synthetic graft and the surrounding native tissue has been reported as a major factor in ultimate failure of the currently used cardiovascular graft replacements. Thus, developing biomaterials that display close mechanical properties as the tissue it is replacing is an important objective in biomedical devices design. Polyvinyl alcohol (PVA) is a biocompatible hydrogel with characteristics desired for biomedical applications. It can be crosslinked by a low temperature thermal cycling process. By using a novel thermal processing method under an applied strain and with the addition of a small amount of bacterial cellulose (BC) nanofibers, an anisotropic PVA-BC nanocomposite was created. The stress-strain tensile properties of porcine aorta were closely matched in both the circumferential and the axial directions by one type of anisotropic PVA-BC nanocomposite (10% PVA with 0.3% BC at 75% initial strain and cycle 2) within physiological range, with improved resistance to further stretch beyond physiological strains. The PVA-BC nanocomposite gives a broad range of mechanical properties, including anisotropy, by controlling material and processing parameters. PVA-BC nanocomposites with controlled degree of anisotropy that closely match the mechanical properties of the soft tissue it might replace, ranging from cardiovascular to other connective tissues, can be created.

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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.054
GPT teacher head0.348
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

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
Published2008
Admission routes1
Has abstractyes

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