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Record W2515642483 · doi:10.1002/app.44348

Surface modification of microcrystalline cellulose (<scp>MCC</scp>) and its application in <scp>LDPE</scp>‐based composites

2016· article· en· W2515642483 on OpenAlexaff
Ariane Chartrand, Jean‐Michel Lavoie, Michel A. Huneault

Bibliographic record

VenueJournal of Applied Polymer Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité de Sherbrooke
FundersCitrus Research Board
KeywordsMaterials scienceLow-density polyethyleneMicrocrystalline celluloseContact angleComposite materialPolyvinyl chlorideSurface modificationUltimate tensile strengthPolymerScanning electron microscopePolyethyleneCelluloseChemical engineering

Abstract

fetched live from OpenAlex

ABSTRACT Microcrystalline cellulose (MCC) was modified by grafting onto its surface ferulic acid, methacryloyl chloride and oleoyl chloride. The efficacy of the chemical modification was confirmed by X‐ray photoelectron spectroscopy. In addition, the size distribution of the cellulosic particles was investigated by optical microscopy and laser granulometry and its hydrophobicity was evaluated using a contact angle method. Finally, to investigate the affinity of modified MCC with a nonpolar polymer and to assess its potential as a biobased reinforcing filler, the modified MCC was compounded into low‐density polyethylene. An organic peroxide, dicumyl peroxide, was added at selected formulations to see if it could further enhance mechanical bonding between the polymer and the particulates. The dispersion was assessed by scanning electron microscopy. Mechanical properties were investigated through tensile testing while the melt rheology of the composites was monitored by small angle oscillatory shear rheology. The acylation modification of the MCC improved the dispersion within LDPE and enhanced the mechanical properties of the composites. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133 , 44348.

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)0.002
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 score0.011
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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