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Predicting of electrical conductivity for Polymer-MXene nanocomposites

Zahra Hadi, Jafar Khademzadeh Yeganeh, Yasser Zare, Muhammad Tajammal Munir, Kyong Yop Rhee

2024Journal of Materials Research and Technology50 citationsDOIOpen Access PDF

Abstract

The prediction of electrical conductivity in MXene nanosheet –polymer composites is a complex task that lacks a straightforward model. In this study, a model is proposed to predict the conductivity for the samples filled with MXene. Our proposed method takes into account various factors that influence the overall conductivity of the samples. The factors include the dimensions of MXene, volume fraction of MXene, percolation onset, tunnel distance, network fraction, and interphase thickness. The validity of the expressed method is examined using experimental data for several samples. Furthermore, an analysis of the relationship between the predicted conductivity and the parameters is conducted to confirm the reliability of the proposed methodology. The calculated outcomes from the proposed model exhibit a high level of accordance with the experimented conductivity of the samples. The maximum electrical conductivity of 16.1 S/m is achieved by the minimum tunneling distance of 1 nm, but the conductivity becomes very weak when tunneling distance in more than 11 nm.

Topics & Concepts

Materials scienceConductivityPercolation (cognitive psychology)Volume fractionElectrical resistivity and conductivityQuantum tunnellingNanocompositeNanosheetPercolation thresholdComposite materialNanotechnologyElectrical engineeringOptoelectronicsPhysical chemistryNeuroscienceChemistryBiologyEngineeringMXene and MAX Phase MaterialsAdvanced Sensor and Energy Harvesting MaterialsGraphene research and applications
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