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Record W3082339808 · doi:10.1002/app.49817

Microinjection molding of polyoxymethylene/multiwalled carbon nanotubes composites with different matrix viscosities

2020· article· en· W3082339808 on OpenAlexafffund
Shengtai Zhou, Xue Lei, Junqi Mei, Andrew N. Hrymak, Musa R. Kamal, Huawei Zou

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMcGill UniversityWestern University
FundersFundamental Research Funds for the Central UniversitiesNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilChina Postdoctoral Science Foundation
KeywordsPolyoxymethyleneMaterials scienceComposite materialCompression moldingMolding (decorative)Carbon nanotubeThermal stabilityPolybutylene terephthalateViscosityPolymerChemical engineeringPolyester

Abstract

fetched live from OpenAlex

Abstract This study features the effect of matrix viscosity on the properties of carbon nanotubes reinforced polyoxymethylene (POM/CNT) microparts, which were obtained via melt blending and subsequent microinjection molding (μIM) processes, under a defined set of processing conditions. Results of compression molding and μIM were compared to assess the influence of processing methods (i.e., thermomechanical history) on the electrical and thermal conductivities, melting and crystallization behavior as well as the thermal degradation resistance of POM/CNT composites. Filler orientation in POM/CNT microparts was evaluated using Raman spectral analysis. The electrical conductivity measurements revealed that matrix viscosity plays a significant role in determining the distribution of CNT. Also, the extreme shearing conditions that prevail in μIM are unfavorable for the construction of random conductive pathways within the micromoldings, as corroborated by transmission electron microscopy. Although the thermal degradation resistance of both POM/CNT composites and corresponding microparts deteriorated with increasing filler concentration, samples prepared with higher matrix viscosity showed higher thermal stability when compared with lower matrix viscosity counterparts. This study provides valuable insights into fabricating multifunctional microparts for potential industrial applications in replacement of metallic components for precision electronic instruments.

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 score0.003
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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

Citations16
Published2020
Admission routes2
Has abstractyes

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