Evidence map›Paper›PMID 36297961›Full record

ArticlePolymers2022

Fabric Insert Injection Molding for the Preparation of Ultra-High Molecular Weight Polyethylene/High-Density Polyethylene Two-Component Self-Reinforced Composites.

Jian Wang, Da Wang, Qianchao Mao, Jinnan Chen

Abstract read
In one paragraph

Article in Polymers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Jian WangState Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China.ORCID 0000-0001-7187-1629
Da WangState Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China.
Qianchao MaoSchool of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Jinnan ChenSchool of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Funding

Fundamental Research Funds for the Central Universities buctrc202005National Natural Science Foundation of China 51873021
6 · The paper itself

Abstract

The fabric insert injection molding approach can be applied to produce easily recyclable self-reinforced polymer composites (SrCs) whose reinforcement and matrix are from the same polymer. However, the mechanical properties of the SrCs are usually limited due to the poor impregnation of the inserted fabric. In this work, the ultra-high molecular weight polyethylene (UHMWPE) fabrics were used as the insert, and the high-density polyethylene (HDPE) melt was injected to fill the mold cavity and impregnate the fabrics. The UHMWPE/HDPE two-component SrCs were prepared. The large difference of melting temperatures between UHMWPE and HDPE can establish a wide processing temperature window, and thus the impregnation of the fabric can be improved by increasing temperature. The tensile strength and modulus of the UHMWPE/HDPE SrCs were up to 148 and 1132 MPa, respectively. The peel strength could be up to 35.2 N/cm. The influences of four main injection molding parameters, including the injection temperature, injection pressure/packing pressure, injection velocity, and packing time, were investigated. The temperature, pressure, viscosity, and density of the matrix in the mold cavity were calculated by the numerical simulation to indicate the impregnation process during the fabric insert injection molding process.

Indexed as

injection moldingmechanical propertiespolyethyleneprocessing parameterself-reinforced composite

Identifiers

PMID36297961
PMCPMC9609700

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Registered trials

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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.