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Record W2317719371 · doi:10.1002/app.43595

Biopolyamide hybrid composites for high performance applications

2016· article· en· W2317719371 on OpenAlexafffund
Shaghayegh Armioun, Suhara Panthapulakkal, Johannes Scheel, Jimi Tjong, Mohini Sain

Bibliographic record

VenueJournal of Applied Polymer Science · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComposite materialMaterials scienceFlexural strengthUltimate tensile strengthFiberFlexural modulusIzod impact strength testCompoundingMolding (decorative)ExtrusionNatural fiberYoung's modulusAbsorption of water

Abstract

fetched live from OpenAlex

ABSTRACT This study focuses on the performance characteristics of wood/short carbon fiber hybrid biopolyamide11 (PA11) composites. The composites were produced by melt‐compounding of the fibers with the polyamide via extrusion and injection molding. The results showed that mechanical properties, such as tensile and flexural strength and modulus of the wood fiber composites were significantly higher than the PA11 and hybridization with carbon fiber further enhanced the performance properties, as well as the thermal resistance of the composites. Compared to wood fiber composites (30% wood fiber), hybridization with carbon fiber (10% wood fiber and 20% carbon fiber) increased the tensile and flexural modulus by 168% and 142%, respectively. Izod impact strength of the hybrid composites exhibited a good improvement compared to wood fiber composites. Thermal properties and resistance to water absorption of the composites were improved by hybridization with carbon fiber. In overall, the study indicated that the developed hybrid composites are promising candidates for high performance applications, where high stiffness and thermal resistance are required. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133 , 43595.

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 score0.027
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations32
Published2016
Admission routes2
Has abstractyes

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