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Record W2071653651 · doi:10.3390/nano3040638

Study of the Effect of Grafting Method on Surface Polarity of Tempo-Oxidized Nanocellulose Using Polycaprolactone as the Modifying Compound: Esterification versus Click-Chemistry

2013· article· en· W2071653651 on OpenAlexafffund
Abdelhaq Benkaddour, Khalil Jradi, Sylvain Robert, Claude Daneault

Bibliographic record

VenueNanomaterials · 2013
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClick chemistryPolycaprolactoneContact angleNanocelluloseX-ray photoelectron spectroscopyGraftingFourier transform infrared spectroscopySurface modificationCycloadditionPolymer chemistryChemistryMaterials scienceChemical engineeringOrganic chemistryNuclear chemistryCelluloseCatalysisPhysical chemistryPolymerComposite material

Abstract

fetched live from OpenAlex

Esterification and click-chemistry were evaluated as surface modification treatments for TEMPO-oxidized nanocelluloses (TONC) using Polycaprolactone-diol (PCL) as modifying compound in order to improve the dispersion of nanofibers in organic media. These two grafting strategies were analyzed and compared. The first consists of grafting directly the PCL onto TONC, and was carried out by esterification between hydroxyl groups of PCL and carboxyl groups of TONC. The second strategy known as click-chemistry is based on the 1,3-dipolar cycloaddition reaction between azides and alkyne terminated moieties to form the triazole ring between PCL and TONC. The grafted samples were characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and Thermogravimetry analysis (TGA). Further, the effects of the two treatments on the surface hydrophobization of TONC were investigated by contact angle measurements. The results show that both methods confirm the success of such a modification and the click reaction was significantly more effective than esterification.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.049
GPT teacher head0.357
Teacher spread0.308 · 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

Citations29
Published2013
Admission routes2
Has abstractyes

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