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Record W2965031504 · doi:10.1039/c9ra01073d

Synergistic toughening of polypropylene with ultra-high molecular weight polyethylene and elastomer-olefin block copolymers

2019· article· en· W2965031504 on OpenAlexaff
Lucheng Qi, Lei Wu, Ren He, Hui Cheng, Bo Liu, Xuelian He

Bibliographic record

VenueRSC Advances · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsPetro-Canada
FundersNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsMaterials scienceCrystallinityPolypropyleneElastomerComposite materialNucleationCopolymerPolyethyleneThermoplastic elastomerCrystallizationIzod impact strength testSpherulite (polymer physics)Polymer chemistryPolymerChemical engineeringChemistryUltimate tensile strength

Abstract

fetched live from OpenAlex

Blends of polypropylene (PP) and ultra-high molecular weight polyethylene (UHMWPE) with elastomer-olefin block copolymers (OBC) were prepared using an ultrasonic twin-screw extruder, and the mechanical, thermal, and rheological properties of the blends were investigated. The interfacial interactions among PP, OBC, and UHMWPE showed that the PP/OBC/UHMWPE blends formed a core-shell structure with UHMWPE as the core and OBC as the shell. The crystallization temperature and the crystallinity of the blends were improved for the heterogeneous nucleation between PP- and OBC-covered UHMWPE particles. Moreover, the mechanical and thermal properties of PP/UHMWPE blends have also been greatly improved by adding OBC. Furthermore, it was evident that the OBC-covered HHMWPE particles became smaller under the application of ultrasonic irradiation, so the interfacial interactions between the particles and the PP matrix were enhanced and the impact strength of the blends was improved.

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)0.000
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 score0.015
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.203
Teacher spread0.199 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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