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Record W3109262079 · doi:10.1021/acs.biomac.0c01267

Assembly of Cellulose Nanocrystal–Lysozyme Composite Films with Varied Lysozyme Morphology

2020· article· en· W3109262079 on OpenAlexfundno aff
Kevin J. De France, Nico Kummer, Qun Ren, Silvia Campioni, Gustav Nyström

Bibliographic record

VenueBiomacromolecules · 2020
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsLysozymeNanocrystalMorphology (biology)Composite numberCelluloseChemical engineeringChemistryMaterials sciencePolymer chemistryNanotechnologyBiochemistryBiologyComposite material

Abstract

fetched live from OpenAlex

In modern society, there is a constant need for developing reliable, sustainable, and cost-effective antibacterial materials. Here, we investigate the preparation of cellulose nanocrystal (CNC)–lysozyme composite films via the well-established method of evaporation-induced self-assembly. We consider the effects of lysozyme concentration and aggregation state (native lysozyme, lysozyme amyloid fibers, and sonicated lysozyme amyloid fibers) on suspension aggregation and film-forming ability. Although at higher lysozyme loading levels (ca. 10 wt %), composite films lost their characteristic chiral nematic structuring, these films demonstrated improved mechanical properties and antibacterial activity with respect to CNC-only films, regardless of lysozyme aggregation state. We anticipate that the results presented herein could also contribute to the preparation of other CNC–protein-based materials, including films, hydrogels, and aerogels, with improved mechanical performance and antibacterial activity.

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 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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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

Citations43
Published2020
Admission routes1
Has abstractyes

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