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Record W2937149737 · doi:10.1002/marc.201900114

Making Nanocomposites of Hydrophilic and Hydrophobic Polymers Using Gas‐Responsive Cellulose Nanocrystals

2019· article· en· W2937149737 on OpenAlexafffund
Farhad Farnia, Weizheng Fan, Yves L. Dory, Yue Zhao

Bibliographic record

VenueMacromolecular Rapid Communications · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceVinyl alcoholNanocompositePolymerCopolymerChemical engineeringToluenePolymer chemistryMethacrylateNanocrystalStyreneContact angleCelluloseComposite materialOrganic chemistryChemistryNanotechnology

Abstract

fetched live from OpenAlex

Abstract Generally, different surface‐functionalized cellulose nanocrystals (CNC) are required for use as nanofillers in hydrophilic and hydrophobic polymers. In the present study, for the first time, a kind of “universal” nanofiller for polymer composites is demonstrated by using CNC grafted with a gas‐responsive polymer. CNC are functionalized with pyrene‐containing poly(2‐( N , N ‐diethylaminoethyl)methacrylate) (CNC‐g‐PDEAEMA‐Py). The reversible transport of the nanocrystals between phase‐separated water and toluene is proved by nuclear magnetic resonance ( 1 H NMR) and fluorescence spectroscopy. On the one hand, water‐soluble poly(vinyl alcohol) (PVA) can be mixed with dispersed CNC upon CO 2 bubbling for making the PVA‐CNC nanocomposite. On the other hand, after the transfer of CNC into the toluene solution upon N 2 bubbling, styrene‐butadiene‐styrene (SBS) triblock copolymer can be dissolved for the SBS‐CNC nanocomposite. In both systems, CNC are well dispersed, having an effect on the mechanical and shape memory properties of SBS and PVA, respectively.

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 score0.021
Threshold uncertainty score0.994

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.312
Teacher spread0.280 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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