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Record W1755667164 · doi:10.1002/app.42520

Influences of the phenolic curative content and blend proportions on the properties of dynamically vulcanized natural rubber/acrylonitrile–butadiene–styrene blends

2015· article· en· W1755667164 on OpenAlexaff
Pinyo Wongthong, Charoen Nakason, Qinmin Pan, Garry L. Rempel, Suda Kiatkamjornwong

Bibliographic record

VenueJournal of Applied Polymer Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Waterloo
FundersRoyal Golden Jubilee (RGJ) Ph.D. ProgrammePrince of Songkla UniversityThailand Research FundChulalongkorn University
KeywordsVulcanizationMaterials scienceNatural rubberComposite materialAcrylonitrileUltimate tensile strengthPolymer blendCuring (chemistry)Thermal stabilityThermoplasticThermoplastic elastomerCopolymerPolymerChemical engineering

Abstract

fetched live from OpenAlex

ABSTRACT The vulcanization of natural rubber (NR)‐blended acrylonitrile–butadiene–styrene (ABS) was carried out with a phenolic curing agent by a melt‐mixing process. The NR compound was first prepared before blending with ABS. The effects of the phenolic curative contents (10, 15, and 20 phr) and blend proportions (NR/ABS ratio = 50 : 50, 60 : 40, and 70 : 30) on the mechanical, dynamic, thermal, and morphological properties of the vulcanized NR/ABS blends were investigated. The tensile strength and hardness of the blends increased with increasing ABS content, whereas the elongation at break decreased. The strength property resulting from the thermoplastic component and the vulcanized NR was an essential component for improving the elasticity of the blends. These blends showed a greater elastic response than the neat ABS. The thermal stability of the blends increased with increasing ABS component. Scanning electron micrographs of the blends showed a two‐phase morphology system. The vulcanized 60 : 40 NR/ABS blend with 15‐phr phenolic resin showed a uniform styrene‐ co ‐acrylonitrile phase dispersed in the vulcanized NR phase; it provided better dispersion between the NR and ABS phases, and this resulted in superior elastic properties. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132 , 42520.

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 categoriesScience and technology studies
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
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 head0.041
GPT teacher head0.235
Teacher spread0.194 · 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.

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
Published2015
Admission routes1
Has abstractyes

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