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Record W2082423585 · doi:10.1155/2009/168041

Antibacterial Properties of Nanosilver PLLA Fibrous Membranes

2009· article· en· W2082423585 on OpenAlexaff
Lin Li, Yi Li, Jiashen Li, Lei Yao, Arthur F. T. Mak, Frank Ko, Ling Qin

Bibliographic record

VenueJournal of Nanomaterials · 2009
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of British Columbia
FundersHong Kong Polytechnic UniversityHong Kong Research Institute of Textiles and ApparelResearch Grants Council, University Grants Committee
KeywordsMembraneMaterials scienceAntibacterial activitySilver nanoparticleNanoparticleComposite materialChemical engineeringNanotechnologyBacteriaChemistry

Abstract

fetched live from OpenAlex

Nanosilver has been studied as a valuable material for it strong antibacterial effects. In this study, we investigated the antibacterial properties of nano silver Poly‐L‐Lactic acid (Ag/PLLA) composite fibrous membranes. Ag/PLLA fibrous membranes were prepared with silver nanoparticles having weight ratio of silver nanoparticles to PLLA at 5% (w/w). In vitro antibacterial tests were performed using Escherichia coli (E. coli) and Staphylococcus aureus (Staph.) to determine the antibacterial capability of the Ag/PLLA fibrous membranes. As the results suggested, Ag/PLLA fibrous membranes showed strong antibacterial properties. Thus, Ag/PLLA fibrous membrane can be used as an antibacterial scaffold for tissue engineering.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.239
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2009
Admission routes1
Has abstractyes

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