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Record W2469603974 · doi:10.1002/pat.3870

Effect of non-rubber components on the mechanical properties of natural rubber

2016· article· en· W2469603974 on OpenAlexfundno aff
Yuanbing Zhou, Kenichiro Kosugi, Yoshimasa Yamamoto, Seiichi Kawahara

Bibliographic record

VenuePolymers for Advanced Technologies · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceRitsumeikan UniversityRyerson University
KeywordsNatural rubberUltimate tensile strengthCrystallizationMaterials scienceFourier transform infrared spectroscopySynthetic rubberChemical engineeringModulusComposite materialDynamic mechanical analysisChemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Effect of the nanomatrix structure on mechanical properties of natural rubber was investigated in relation to the strain-induced crystallization. Structure of natural rubber was analyzed through Fourier transform infrared spectroscopy, wide-angle X-ray diffraction measurement and transmission electron microscopy. The nanomatrix of the non-rubber components was found to be inevitably formed in natural rubber, in which natural rubber particles linking to fatty acids were dispersed in the nanomatrix of the proteins and phospholipids. The nanomatrix disappeared after deproteinization of natural rubber with urea. Tensile strength and modulus of natural rubber were reduced by removal of the fatty acids and the proteins, which resulted in disappearance of the nanomatrix structure. The effect of fatty acids on the crystallization of natural rubber in small particles as a dispersoid was proved by tensile test of blend of natural rubber and styrene butadiene rubber. Copyright © 2016 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations85
Published2016
Admission routes1
Has abstractyes

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