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Record W2029987117 · doi:10.1002/app.28217

Morphology and mechanical properties of natural rubber and styrene‐grafted natural rubber latex compounds

2008· article· en· W2029987117 on OpenAlexaff
Wanvimon Arayapranee, Garry L. Rempel

Bibliographic record

VenueJournal of Applied Polymer Science · 2008
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStyreneNatural rubberMaterials scienceCopolymerUltimate tensile strengthStyrene-butadieneComposite materialPolystyrenePolymerPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract The mechanical properties of natural rubber latex (NRL) modified with styrene‐grafted natural rubber (styrene‐GNR) latex were investigated. Styrene‐GNR was first synthesized via emulsion copolymerization using cumene hydroperoxide/tetraethylene pentamine as an initiator. The styrene‐GNR latex produced was mixed with NRL with various latex compounding contents and then prevulcanization was carried out. The mechanical properties and heat, weathering, and ozone resistance of the natural rubber (NR) and styrene‐GNR latex compounds were investigated as a function of the grafted NR content. The results indicated that the tensile and tear strength were decreased, whereas Young's modulus and hardness were increased at high content of styrene‐GNR. Addition of styrene‐GNR improved the resistance of the compounds to heat and weathering ageing. The ozone resistance of the compound containing styrene‐GNR is superior to that of the NR‐rich compound. The results indicated that NR/styrene‐GNR latex compounds maintained good antiageing properties required for outdoor applications. The tensile fracture surface examined by scanning electron microscopy confirmed a shift from ductility failure to brittle with an increase of the styrene‐GNR content in the compounds. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

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 score0.014
Threshold uncertainty score0.867

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 studies0.0000.002
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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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
Published2008
Admission routes1
Has abstractyes

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