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Record W2517027842 · doi:10.1021/acssuschemeng.5b00380

Polyrhodanine Coated Cellulose Nanocrystals: A Sustainable Antimicrobial Agent

2015· article· en· W2517027842 on OpenAlexafffund
Juntao Tang, Yang Song, Shazia Tanvir, William A. Anderson, Richard M. Berry, Kam Chiu Tam

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsCelluForce (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAntimicrobialNanoparticleMaterials scienceNanocompositeMonomerChemical engineeringCoatingNanocrystalCellulosePolymerizationNanotechnologyChemistryPolymerNuclear chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

This paper reports on a simple and green approach to fabricate cellulose nanocrystal@polyrhodanine (CNC@PR) core–sheath nanoparticles. Polymerization of rhodanine on the surface of negatively charged CNC was achieved using ferric chloride as the initiator and oxidant. The coating conditions were optimized by varying the ratio of CNC and monomer as well as the concentration of oxidant. Antimicrobial tests were performed using Escherichia coli (Gram negative) and Bacillus subtilis (Gram positive) as model bacteria and the minimum inhibitory concentrations were determined by plate colony counting methods. Rod-like CNC@PR nanoparticles exhibited promising antimicrobial properties, comparable to spherical nanocomposite particles. This may be attributed to the lower percolation threshold for rod-like nanoparticles resulting from the higher aspect ratio. By taking advantage of the nanosize effects, the core–sheath material can be a potential candidate for antimicrobial applications, such as food-packaging, antimicrobial additives and antimicrobial surfaces or coatings.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations64
Published2015
Admission routes2
Has abstractyes

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