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Record W3103264390 · doi:10.1177/0021955x20969553

Synergetic effect of crystal nucleating agent and melt self-enhancement of isotactic polypropylene on its rheological and microcellular foaming properties

2020· article· en· W3103264390 on OpenAlexaff
Xiaoli Zhang, Xihuan Wang, Binbin Dong, Guoqiang Zheng, Jingbo Chen, Changyu Shen, Chul B. Park

Bibliographic record

VenueJournal of Cellular Plastics · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceTacticityComposite materialNucleationRheologyCrystallizationPolypropyleneUltimate tensile strengthMelt flow indexChemical engineeringPolymerCopolymerPolymerization

Abstract

fetched live from OpenAlex

Crystal nucleating agent Bis (3, 4- dimethylbenzylidene) sorbitol (DMDBS) was used to tune the melt strength and microcellular foaming properties of isotactic polypropylene (iPP) in this study. Rheological testing results reveal that the introduction of DMDBS could enhance the storage modulus and complex viscosity of iPP, obviously increase its crystallization onset temperature, compared to its counterparts without DMDBS. The addition of DMDBS could also significantly increase the cell nucleating ability of iPP, due to its large surface, cooperating with a thermal history control treatment. Quite fine microcellular iPP/DMDBS foams were fabricated with relatively small average cell sizes of nano to several micrometers, and cell densities up to 10 11 ∼10 12 cells/cm 3 , using the synergy effect of DMDBS and iPP’s melt self-enhancement. Under a comparatively low re-saturation pressure of 8 to 12 MPa, ideal microcellular foams could be generated, at a temperature zone of 158 to 162°C, which is slightly below to iPP’s original pellets nominal melting point.

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 score0.009
Threshold uncertainty score0.487

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 head0.015
GPT teacher head0.202
Teacher spread0.187 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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