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Record W1963091146 · doi:10.1002/app.42438

Polypropylene reinforced with nanocrystalline cellulose: Coupling agent optimization

2015· article· en· W1963091146 on OpenAlexafffund
Hojjat Mahi Hassanabadi, Ayşe Alemdar, Denis Rodrigue

Bibliographic record

VenueJournal of Applied Polymer Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsFPInnovationsCRB Innovations (Canada)Université Laval
FundersNatural Sciences and Engineering Research Council of CanadaKarlsruhe Institute of TechnologyFonds Québécois de la Recherche sur la Nature et les TechnologiesMinistero dello Sviluppo EconomicoFPInnovationsArboraNano
KeywordsMaterials sciencePolypropyleneComposite materialMaleic anhydrideRheologyNanocrystalline materialCelluloseNanocompositePolymerCopolymerChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

ABSTRACT Five different grades of maleic anhydride polypropylene (MAPP) having different molecular weight and acid value (AV) were used as coupler in PP‐nanocrystalline cellulose (NCC) composites. The main objective was to study the effect of MAPP structure (Mw, AV) and filler/coupler ( F / C ) ratio on mechanical properties in order to find optimum mechanical properties in tension, flexion, and impact. Results showed that both Mw and AV have direct effect on mechanical properties and a balance between both must be achieved to get the best performance. However, regardless of MAPP structure, optimum improvement was obtained for F / C = 7.5/1. Shear rheological data showed that at high MAPP content, MAPP acts as lubricant. DSC and AFM analysis showed small reduction in the size of PP crystals in the presence of NCC. Rheological data under large amplitude oscillatory shear showed that the nanocomposites used here are under percolation. Using these analyses, possible reinforcement mechanisms were investigated. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 42438.

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 score0.187
Threshold uncertainty score0.545

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 head0.023
GPT teacher head0.272
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

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

Citations31
Published2015
Admission routes2
Has abstractyes

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