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Record W2783113153 · doi:10.1021/acs.chemmater.7b03939

CO<sub>2</sub>-Switchable Cellulose Nanocrystal Hydrogels

2018· article· es· W2783113153 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

VenueChemistry of Materials · 2018
Typearticle
Languagees
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsFPInnovationsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsMaterials scienceSuspension (topology)Chemical engineeringNanocrystalRheologyCelluloseImidazoleAdsorptionViscosityNanotechnologyPolymer chemistryChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A carbon-dioxide-switchable (CO 2 -switchable) hydrogel was prepared by adding imidazole to a suspension of cellulose nanocrystals (CNCs). Sparging of CO 2 through the imidazole-containing CNC suspension led to gelation of the CNCs, which was reversible by subsequent sparging with nitrogen (N 2 ) to form a low-viscosity CNC suspension. The gelation process and the properties of the hydrogels have been investigated by rheology, ζ potential, pH, and conductivity measurements, and the gels were found to have interesting and reversible tunable mechanical properties. The present work describes a straightforward way to obtain switchable CNC hydrogels without the need to functionalize CNCs or add strong acids or bases. These CO 2 -responsive CNC hydrogels have potential for application in stimuli-responsive adsorbents, filters, and flocculants.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.277
Teacher spread0.262 · 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