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Record W1987595510 · doi:10.1039/c4tb01647e

Enhanced colloidal stability and antibacterial performance of silver nanoparticles/cellulose nanocrystal hybrids

2014· article· en· W1987595510 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Materials Chemistry B · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsRegional Municipality of WaterlooCelluForce (Canada)National Institute for NanotechnologyUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanocrystalMaterials scienceColloidCelluloseNanoparticleNanotechnologySilver nanoparticleChemical engineering

Abstract

fetched live from OpenAlex

The aggregation of nanoparticles has been shown to significantly reduce the activity of nanomaterials, resulting in inferior performance. As an alternative to the use of traditional capping agents, stabilization of unstable nanoparticles with water-dispersible and biocompatible carriers is a promising strategy. A bioinspired coating strategy was developed and the hybrid nanoparticles displayed excellent colloidal stability that significantly improved antibacterial activity when silver nanoparticles (AgNPs) were used as a model. Cellulose nanocrystals (CNCs) were first modified with dopamine, followed by in situ generation and anchoring of AgNPs on the surface of CNCs through the reduction of silver ions by polydopamine coated CNCs. The results indicated that the dispersion stability of AgNPs was significantly enhanced by the CNC, which in turn resulted in more than fourfold increase in antibacterial activity based on antibacterial studies using Escherichia coli and Bacillus subtilis.

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.

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 categoriesInsufficient 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 score0.002
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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