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Record W3152795746 · doi:10.1063/5.0046291

Cationic and anionic cellulose nanocrystalline (CNC) hydrogels: A rheological study

2021· article· en· W3152795746 on OpenAlexafffund
Parya Keyvani, Kudzanai Nyamayaro, Parisa Mehrkhodavandi, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRheologyFlocculationCationic polymerizationShearing (physics)CelluloseNanocrystalline materialSelf-healing hydrogelsChemical engineeringMaterials scienceComposite materialShear (geology)Polymer chemistryNanotechnology

Abstract

fetched live from OpenAlex

Although the rheology of cellulose nanocrystalline (CNC) suspensions has been widely studied, less attention has been paid to the modified cellulose nanocrystals such as cationic and anionic cellulose hydrogels. In this work, the rheological behavior of cellulose nanocrystals (CNCs), anionic CNCs (pCNCs), and cationic CNCs (nCNCs), was comparatively studied. The rheological behavior demonstrated that the nCNC and pCNC form hydrogen bonding, which significantly contributes to the increase in the gel strengths in the sonicated state. The formation of such structures between individual fibers prevents flocculation due to the increased suspension stability. In addition, the extensive formation of hydrogen bonding in the case of nCNC compared to that of pCNC explains its enhanced rheological properties. The effect of pre-shear has been studied in detail for these systems by considering a combination of pre-shear and startup of steady shear in different shearing directions with certain rest/recovery time in between to eliminate strain history and thus eliminate the possible bias of pre-shearing on structure formation.

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 categoriesnone
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 score0.019
Threshold uncertainty score0.517

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)0.0000.000

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 head0.032
GPT teacher head0.301
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2021
Admission routes2
Has abstractyes

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