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Record W2950652498 · doi:10.1021/acs.biomac.9b00646

Functionalizing Cellulose Nanocrystals with Click Modifiable Carbohydrate-Binding Modules

2019· article· en· W2950652498 on OpenAlexafffund
Kevin Aïssa, Muzaffer A. Karaaslan, Scott Renneckar, Jack Saddler

Bibliographic record

VenueBiomacromolecules · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbohydrateCelluloseNanocrystalClick chemistryChemistryNanotechnologyMaterials scienceChemical engineeringCombinatorial chemistryBiochemistry

Abstract

fetched live from OpenAlex

Functionalized cellulose nanocrystals (CNC) have unique properties that make them attractive in various applications such as drug delivery, hydrogels, and emulsions. However, the predominant chemical methods currently used to functionalize cellulose nanocrystals have a large environmental footprint. Although greener methods are desirable, the relatively inert nature of cellulose crystals presents a major challenge to their potential modification in aqueous media. In the work reported here, carbohydrate binding modules (CBMs) were used to introduce new functionality to cellulose surfaces. CBM2a, which has a strong affinity for crystalline cellulose, was functionalized with an alkyne at the terminal amine position. The alkyne group, which was introduced onto the cellulose surface with CBM2a, underwent a Click reaction with polyethylene glycol (PEG) to modify CNC surfaces. This provided a strong, non-covalent modification of cellulose surfaces that was carried out in a one-pot reaction in aqueous media. The CBM-PEG modification of cellulose surfaces increased CNC redispersion after drying and improved suspension stability based on steric interactions. It was apparent that hybrid polysaccharide-protein, self-assembled nanoparticles could be effectively produced, with potential for nanomedicine, immunoassay, and drug delivery applications.

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), Insufficient 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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.002

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 head0.016
GPT teacher head0.253
Teacher spread0.236 · 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

Citations21
Published2019
Admission routes2
Has abstractyes

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