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Record W3109079717 · doi:10.1002/pen.25602

Microstructure and orientation evolution of microinjection molded β‐nucleated isotactic polypropylene/poly(ethylene terephthalate) blends

2020· article· en· W3109079717 on OpenAlexaff
Zhongguo Zhao, Shengtai Zhou, Andrew N. Hrymak, Musa R. Kamal, Taotao Ai

Bibliographic record

VenuePolymer Engineering and Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsMcGill UniversityWestern University
FundersChina Postdoctoral Science Foundation
KeywordsMaterials scienceShearing (physics)TacticityComposite materialNucleationMicrostructureLamellar structureMolding (decorative)PolyesterPolymerPolymerization

Abstract

fetched live from OpenAlex

Abstract The influence of different shear stresses induced by changing injection molding speeds on molecular chain orientation and lamellar branching of β‐nucleated iPP/poly(ethylene terephthalate) (PET) microparts was investigated using two‐dimensional wide‐angle X‐ray diffraction and 2D‐small‐angle X‐ray scattering. Results indicated that the prevailing shear stress can promote the formation of parent–daughter α‐crystal structure and twisted shish–kebab structure in subsequent microparts. The diffraction of (300) plane of β‐crystals was also observed at varying injection speeds. Increasing injection speeds can significantly enhance the content of β‐crystals from 24 to 41% for β‐nucleated iPP microparts. Additionally, the content of β‐crystals was further enhanced in β‐nucleated iPP/PET microparts with in situ formation of PET microfibrils under intensive shearing conditions. The addition of both PET and β‐nucleation agents coupling with high shearing conditions exerts a synergetic effect on the development of β‐crystals. However, the orientation degree of crystal lattice decreased with increasing injection speeds for both β‐nucleated iPP and iPP/PET microparts.

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 score0.018
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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