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Record W2502062256 · doi:10.1021/acs.biomac.6b00906

Hydrothermal Gelation of Aqueous Cellulose Nanocrystal Suspensions

2016· article· en· W2502062256 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomacromolecules · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersFPInnovationsNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSelf-healing hydrogelsBiocompatibilityRheologyChemical engineeringNanocrystalHydrothermal circulationAqueous solutionCelluloseMaterials scienceDynamic mechanical analysisViscosityPorosityChemistryNanotechnologyPolymer chemistryOrganic chemistryComposite materialPolymer

Abstract

fetched live from OpenAlex

We report the facile preparation of gels from the hydrothermal treatment of suspensions of cellulose nanocrystals (CNCs). The properties of the hydrogels have been investigated by rheology, electron microscopy, and spectroscopy with respect to variation in the temperature, time, and CNC concentration used in preparation. Desulfation of the CNCs at high temperature appears to be responsible for the gelation of the CNCs, giving highly porous networks. The viscosity and storage modulus of the gels was shown to increase when samples were prepared at higher treatment temperature. Considering the wide natural abundance and biocompatibility of CNCs, this simple, green approach to CNC-based hydrogels is attractive for producing materials that can be used in drug delivery, insulation, and as tissue scaffolds.

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.

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 score0.008
Threshold uncertainty score0.645

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)0.0010.000

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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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