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Record W2050902524 · doi:10.1177/0021998314531034

The role of nanoclay formations and wood fiber levels on central composite designed polyethylene composites

2014· article· en· W2050902524 on OpenAlexafffund
Ruijun Gu, Mohini Sain, B. V. Kokta, Kwei-Nam Law

Bibliographic record

VenueJournal of Composite Materials · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité du Québec à Trois-RivièresUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialMasterbatchComposite numberPolyethyleneFiberScanning electron microscopeNatural fiberIzod impact strength testNanocompositeUltimate tensile strength

Abstract

fetched live from OpenAlex

Central composite designed experiments are conducted to study the independent effects of maleated polyethylene, dicumyl peroxide and nanoclay in the forms of natural (Cloisite® Na + ) and masterbatch (Nanoblend™ concentrates MB2001) on the mechanical properties of fiber reinforced PE composites with different fiber levels. The optimum values are predicted on the results of designed experiments and there are linear regressions between fiber content and their mechanical properties. The deposition and formation of nanoclay particles in PE composites are ascertained by scanning electron microscope and transmission electron microscope observations. Both natural nanoclay and MB2001 can be delaminated and even exfoliated in polarized PE matrix. As wood fiber is introduced, natural nanoclay particles (nanoclay-natural) are deposited on fiber surface even loaded in fiber lumens, but the nanoclay particles of MB2001 (nanoclay-concentrate) are mostly dispersed in the matrix. In addition, the different reinforcements between nanoclay-natural and nanoclay-concentrates are also investigated to find out the influence of particle formation on the quality of composite materials.

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)0.001
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 score0.006
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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