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Record W3157149366 · doi:10.6000/1929-5995.2021.10.1

Introduction to the Study of Mechanical Properties of Terpolymer PP/EPDM Mixtures

2021· article· en· W3157149366 on OpenAlexvenueno aff
Sandra R. Scagliusi, Elizabeth C.L. Carvalho, Ademar B. Lugão

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceIzod impact strength testPolypropyleneMelt flow indexComposite materialElastomerThermoplastic elastomerThermoplasticDifferential scanning calorimetryThermogravimetric analysisEPDM rubberCopolymerNatural rubberPolymerChemical engineeringUltimate tensile strength

Abstract

fetched live from OpenAlex

Thermoplastic elastomers (TPEs), based in PP (Polypropylene) / EPDM (Ethylene Propylene Diene Monomer) have as purpose improving PP resistance and impact, aiming to a more comprehensive use in automotive market, among edifications, construction and packaging sectors, due to their recyclability properties. PP is a commodity, with a high melting point, high mechanical resistance and low density, posing a balance between physical and mechanical properties; in addition, it shows an easy processing, at low cost. In order to minimize this deficiency, EPDM, an impact modifier, can be used. Nevertheless, most of polymeric blends are incompatible and immiscible, i.e., show a mutual and limited solubility and in most of cases, a high interfacial tension. However, there is a relatively low interfacial tension (force which acts on transformation of a continuous structure in a dispersion) between PP and EPDM (approximately 0.3 mN.m-1), reducing the rate of breakup and facilitating the build-up of a continuous structure. This work aims to the study of compatibility of PP and EPDM blends and variation of mechanical properties, emphasizing that many properties of thermoplastic elastomers can be processed according with conventional thermoplastics methods: herein, PP/EPDM blends, 90/10, 80/20, 70/30 and 50/50 were characterized according to: Mechanical essays, Differential Scanning Calorimetry, Thermogravimetric Analyses, Melt Flow Index, Izod Impact Strength and Dynamic mechanical Analyses.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.043
GPT teacher head0.340
Teacher spread0.296 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of Research Updates in Polymer ScienceSame topicPolymer Nanocomposites and PropertiesFrench-language works237,207