What is it about?
Composites are reinforced to improve its physical, chemical or mechanical properties. In this nanocomposite, addition of carbon nanotubes [CNTs] fillers to the polyurethane matrix increases its mechanical properties and electrical conductivity. Achieving good electrical conductivity with optimum filling is important as this composite will serve as a shape memory system. We introduce a novel modelling approach which can predict this percolation effect. Further, shape memory effect was evaluated and recovery efficiency is reported.
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Why is it important?
This paper provides a simple mathematical approach to predict percolation effect. This will reduce the number of trials required in manufacturing of such nanocomposites.
Perspectives
Contributing to this article was a great pleasure as the co-authors were my research supervisors. I formulated the pseduo - modelling approach presented in this article with their esteem guidance. This article eventually provided me with good collaborations and ultimately to a greater involvement in computational materials science.
Mr. Arun Kumar Rajasekaran
National Institute of Technology Tiruchirappalli
Read the Original
This page is a summary of: Structural-modelling and experimental validation of percolation threshold for nanotube-polyurethane shape memory system, Materials Science and Technology, September 2019, Taylor & Francis,
DOI: 10.1080/02670836.2019.1661660.
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