Evidence map›Paper›PMID 31671568›Full record

ArticlePolymers2019

Effects of Three Different Injection-Molding Methods on the Mechanical Properties and Electrical Conductivity of Carbon Nanotube/Polyethylene/Polyamide 6 Nanocomposite.

Dashan Mi, Zhongguo Zhao, Wenli Zhu

Abstract read
In one paragraph

Article in Polymers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dashan MiDepartment of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China. midashan@126.com.ORCID 0000-0003-0792-0718
Zhongguo ZhaoSchool of Polymer Science and Engineering, Shaanxi University of Technology, Hanzhong 723001, China. zhaozhongguo@snut.edu.cn.
Wenli ZhuDepartment of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China. zwl829@126.com.

Funding

Foundation of Hubei University Arts and Science 2059071National Natural Science Foundation of China 51403059
6 · The paper itself

Abstract

Morphological evolution under shear, during different injection processes, is an important issue in the phase morphology control, electrical conductivity, and physical properties of immiscible polymer blends. In the current work, conductive nanocomposites were produced through three different injection-molding methods, namely, conventional injection molding, multi-flow vibration injection molding (MFVIM), and pressure vibration injection molding (PVIM). Carbon nanotubes in the polyamide (PA) phase and the morphology of the PA phase were controlled by various injection methods. For MFVIM, multi-flows provided consistently stable shear forces, and mechanical properties were considerably improved after the application of high shear stress. Shear forces improved electrical property along the flow direction by forming an oriented conductive path. However, shear does not always promote the formation of conductive paths. Oscillatory shear stress from a vibration system of PVIM can tear a conductive path, thereby reducing electrical conductivity by six orders of magnitude. Although unstable high shear forces can greatly improve mechanical properties compared with the conventional injection molding (CIM) sample, oscillatory shear stress increases the dispersion of the PA phase. These interesting results provide insights into the production of nanocomposites with high mechanical properties and suitable electrical conductivity by efficient injection molding.

Indexed as

electrical conductivityinjection moldingmechanical propertiesnanocomposites

Identifiers

PMID31671568
PMCPMC6918180

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.