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Record W2324618612 · doi:10.1515/hf-2012-0206

Polyelectrolyte complex containing antimicrobial guanidine-based polymer and its adsorption on cellulose fibers

2013· article· en· W2324618612 on OpenAlexaff
Liying Qian, Chao Dong, Xiangtao Liang, Beihai He, Huining Xiao

Bibliographic record

VenueHolzforschung · 2013
Typearticle
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCationic polymerizationPolyelectrolyteAdsorptionChemical engineeringGuanidineCellulosePolymerPolymer chemistryMaterials scienceChemistryCarboxymethyl celluloseOrganic chemistrySodium

Abstract

fetched live from OpenAlex

Abstract Polyelectrolyte (PE) complexes (PECs) are formed by the electronic interaction between cationic and anionic PEs, and a number of factors influence the forming pattern and characteristic of the PECs. In this work, a guanidine-based polymer with high cationic charge density (CD) and low molecular weight (MW) was applied for interacting with anionic carboxymethylcellulose (CMC) with low CD and high MW. To reveal the self-assembly pattern of the PEC, the turbidity of PEC and layer-by-layer (LBL) film, along with its adsorption on cellulose fibers, was characterized. The antimicrobial activity of the handsheet containing the PEC was also investigated. The charge ratio of anionic PE to cationic PE was found to be critical to the PEC stability. The roughness of the LBL film was increased and then decreased with more bilayers assembled. The isothermal adsorption indicated that the amount of adsorbed cationic PE on cellulose fibers was increased significantly by interacting with anionic CMC. The inhibition of the cationic PE on bacterial growth was not impaired by the formation of the complex. The CMC with high MW in the complex could maintain or even improve the antimicrobial efficiency of the guanidine-based polymer in handsheet.

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)0.000
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 score0.008
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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