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Record W1987251305 · doi:10.1021/bm5001444

Effect of Cellulose Nanowhiskers on Surface Morphology, Mechanical Properties, and Cell Adhesion of Melt-Drawn Polylactic Acid Fibers

2014· article· en· W1987251305 on OpenAlexaff
Kazi M. Zakir Hossain, Muhammad S. Hasan, Daniel Boyd, C.D. Rudd, Ifty Ahmed, Wim Thielemans

Bibliographic record

VenueBiomacromolecules · 2014
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsDalhousie University
FundersEngineering and Physical Sciences Research Council
KeywordsPolylactic acidMaterials scienceComposite materialFiberCoatingSurface roughnessAdhesionCellulose acetateMorphology (biology)CelluloseChemical engineeringPolymer

Abstract

fetched live from OpenAlex

Polylactic acid (PLA) fibers were produced with an average diameter of 11.2 (± 0.9) μm via a melt-drawing process. The surface of the PLA fibers was coated with blends of cellulose nanowhiskers (CNWs) (65 to 95 wt %) and polyvinyl acetate (PVAc). The CNWs bound to the smooth PLA fiber surface imparted roughness, with the degree of roughness depending on the coating blend used. The fiber tensile modulus increased 45% to 7 GPa after coating with 75 wt % CNWs compared with the uncoated PLA fibers, and a significant increase in the fiber moisture absorption properties at different humidity levels was also determined. Cytocompatibility studies using NIH-3T3 mouse fibroblast cells cultured onto CNWs-coated PLA surface revealed improved cell adhesion compared with the PLA control, making this CNW surface treatment applicable for biomedical and tissue engineering applications. Initial studies also showed complete cell coverage within 2 days.

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 score0.007
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.212
Teacher spread0.199 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

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