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Record W2162647413 · doi:10.3139/217.0031

Influence of Viscosity-interface Modifier Interactions on Performance and Processability of Rice Hull PE Composites

2006· article· en· W2162647413 on OpenAlexaff
Nagaraj Dixit, Mohini Sain, M. T. Kortschott, Deepaksh Gulati

Bibliographic record

VenueInternational Polymer Processing · 2006
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceComposite materialPolyolefinFlexural strengthUltimate tensile strengthFlexural modulusViscosityPolyethyleneIzod impact strength testLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Interface modifiers and viscosity modifiers are added to wood plastic composites to improve interfacial adhesion and processing rate respectively. However, because of the chemically reactive nature of interface modifiers and viscosity modifiers, there is a high possibility of interactions between them, influencing the mechanical properties and processabilty of the composites. To evaluate this point, interactions between three different interface modifiers and a viscosity modifier were investigated in this study. Concurrently, the effectiveness of three different interface modifiers in improving mechanical properties of the composites was also studied. The results indicated that there was a significant improvement in the tensile strength, flexural strength, flexural modulus and impact properties of the composites with all the three maleated polyethylene based coupling agents. Moreover, presence of an amide carboxylic acid based viscosity modifier along with a maleated polyolefin based interface modifier reduced the mechanical properties of the composites as compared with that of the composites having only maleated polyolefin based interface modifiers.

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 score0.014
Threshold uncertainty score0.586

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.268
Teacher spread0.259 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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