A Morphology Study of Nanofiller Networks in Polymer Nanocomposites: Improving Their Electrical Conductivity through Better Doping Strategies

  • Angel Mora Cordova

Student thesis: Doctoral Thesis


Over the past years, research efforts have focused on adding highly conductive nanoparticles, such as carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs), into polymers to improve their electrical conductivity or to tailor their piezoresistive behavior. Resultant materials are typically described by the weight or volume fractions of their nanoparticles. The weight/volume fraction alone is a very global quantity, making it a poor evaluator of a doping configuration. Knowing which particles actually participate in improving electrical conductivity can optimize the doping strategy. Additionally, conductive particles are only capable of charge transfer over a very short range, thus most of them do not form part of the conduction path. Thus, understanding how these particles are arranged is necessary to increase their efficiency. First, this work focuses on polymers loaded with CNTs. A computational modeling strategy based on a full morphological analysis of the CNT network is presented to systematically analyze conductive networks and show how particles are arranged. A definition of loading efficiency is provided based on the results obtained from this morphology analysis. This study provides useful guidelines for designing these types of materials based on important features, such as representative volume element, nanotube tortuosity and length, tunneling cutoff distance, and efficiency. Second, a computational approach is followed to study the conductive network formed by hybrid particles in polymer nanocomposites. These hybrid particles are synthesized by growing CNTs on the surfaces of GNPs. The objective of this study is to show that the higher electrical conductivity of these composites is due to the hybrids forming a segregated structure. Polymers loaded with hybrid particles have shown a higher electrical conductivity compared with classical carbon fillers: only CNTs, only GNPs or mixed CNTs and GNPs. This is done to understand and compare the doping efficiency of the different types of nanoparticles. Finally, some parameters of the hybrid particle are studied: CNT density on GNPs, and CNT and GNP geometries. Recommendations to further improve the composite’s conductivity based on these parameters are presented. It is noted that this work is the first time the hybrid particle is studied through a computational approach.
Date of AwardFeb 2018
Original languageEnglish (US)
Awarding Institution
  • Physical Sciences and Engineering
SupervisorBrian Moran (Supervisor) & Gilles Lubineau (Supervisor)


  • carbon nanotubes
  • Graphene nanoplatelets
  • Hybrid particles
  • Polymer composites
  • Computational modeling
  • Electrical conductivity

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