Evidence map›Paper›PMID 35808504›Full record

ArticleSensors (Basel, Switzerland)2022

A Novel Approach to Non-Destructive Rubber Vulcanization Monitoring by the Transient Radar Method.

Salar Tayebi, Ali Pourkazemi, Nicolas Ospitia Patino, Kato Thibaut, Olsi Kamami, Johan Stiens

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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. Article
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

6 authors.

Salar TayebiDepartment of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.ORCID 0000-0001-6751-1858
Ali PourkazemiDepartment of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Nicolas Ospitia PatinoDepartment of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.ORCID 0000-0001-6116-5763
Kato ThibautDepartment of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Olsi KamamiDepartment of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.
Johan StiensDepartment of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium.ORCID 0000-0001-5049-7885

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rubber is one of the most used materials in the world; however, raw rubber shows a relatively very low mechanical strength. Therefore, it needs to be cured before its ultimate applicatios. Curing process specifications, such as the curing time and temperature, influence the material properties of the final cured product. The transient radar method (TRM) is introduced as an alternative for vulcanization monitoring in this study. Three polyurethane-rubber samples with different curing times of 2, 4, and 5.5 min were studied by TRM to investigate the feasibility and robustness of the TRM in curing time monitoring. Additionally, the mechanical stiffness of the samples was investigated by using a unidirectional tensile test to investigate the potential correlations between curing time, dielectric permittivity, and stiffness. According to the results, the complex permittivity and stiffness of the samples with 2, 4, and 5.5 min of curing time was 17.33 ± 0.07 - (2.41 ± 0.04)j; 17.09 ± 0.05 - (4.90 ± 0.03)j; 23.60 ± 0.05 - (14.06 ± 0.06)j; and 0.29, 0.35, and 0.38 kPa, respectively. Further statistical analyses showed a correlation coefficient of 0.99 (

Indexed as

RadarRubberPolyurethanesTemperaturePolyurethanesRubbercomplex permittivitycuring timenon-destructiveTRMvulcanization monitoring

Identifiers

PMID35808504
PMCPMC9269798

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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.