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Record W2965176276 · doi:10.1002/app.48295

<i>In situ</i> compatibilization of a polyethylene, polypropylene, and polystyrene ternary blend through Friedel–Crafts alkylation

2019· article· en· W2965176276 on OpenAlexaff
VINICIUS LUIZ DE CARVALHO, Camila Safieddine, Nicole R. Demarquette, Luís Antônio Pinheiro

Bibliographic record

VenueJournal of Applied Polymer Science · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsÉcole de Technologie Supérieure
FundersFundação AraucáriaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCompatibilizationMaterials sciencePolystyrenePolypropyleneHigh-density polyethylenePolyethylenePolymer blendPolymer chemistryTernary operationUltimate tensile strengthComposite materialPhase (matter)PolymerOrganic chemistryCopolymerChemistry

Abstract

fetched live from OpenAlex

ABSTRACT The Friedel–Crafts alkylation reaction has been applied to reactively compatibilize a ternary blend of high‐density polyethylene (HDPE), polypropylene (PP), and polystyrene (PS). The reactions were carried out in an internal mixer using varying catalyst concentrations. The resulting compatibilizer was quantified after Soxhlet extraction. In addition, p ‐substitution due to the grafting of alkyl groups onto the PS benzene ring was identified via nuclear magnetic resonance spectroscopy. The size of the PS domain in the reactive compositions is decreased by 80%. Moreover, the phase in which PS droplets were dispersed varied, that is, in the nonreactive blends they were found in the PP phase and in the reactive blends they shifted toward the HDPE phase. The effect of the compatibilizing agent was to improve the mechanical properties of the blend. Even with the lowest catalyst content, the properties of elongation‐at‐break, tensile strength, toughness, and elastic modulus showed improvements. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137 , 48295.

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 score0.008
Threshold uncertainty score0.622

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 studies0.0000.000
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 head0.009
GPT teacher head0.237
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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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