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Effect of NR/ABS Ratio on Mechanical, Dynamical and Morphological Properties of Vulcanized NR/ABS Blends

2013· article· en· W2059149042 on OpenAlexaff
Pinyo Wongthong, Charoen Nakason, Qin Pan, Garry L. Rempel, Suda Kiatkamjornwong

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Waterloo
FundersThailand Research FundChulalongkorn University
KeywordsVulcanizationMaterials scienceComposite materialUltimate tensile strengthThermoplasticCuring (chemistry)Natural rubberElongationModulusThermoplastic elastomerDynamic mechanical analysisPolymerCopolymer

Abstract

fetched live from OpenAlex

Dynamically vulcanized NR/ABS blends by phenolic curing were carried out using an internal mixer at 180 °C. The effect of NR/ABS ratio (i.e., 50/50, 60/40, 70/30) on mechanical, dynamical and morphological properties was investigated. It was found that the tensile strength and hardness increased with an increase in ABS loading whilst the elongation at break decreased, indicating that the strength and hardness attributed to thermoplastic component whereas the elasticity of the blends contributed to the vulcanized NR. The results showed that the 60/40 NR/ABS blend provided the well-balanced mechanical properties. The storage modulus of thermoplastic vulcanizates (TPVs) increased with increasing contents of rubber. This is due to that a higher content of vulcanized NR domain was dispersed in the ABS matrix. The results agreed well with the tan δ and complex viscosity of the TPV at various blend ratios. SEM micrographs of 60/40 NR/ABS blend showed that vulcanized rubber domains were dispersed in a continuous ABS phase and it contributed to greater interfacial adhesion between the two phases and thus provided superior properties.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.046
GPT teacher head0.315
Teacher spread0.269 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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