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Supposition of Graphene Stacks to Estimate the Contact Resistance and Conductivity of Nanocomposites Publisher



Zare Y1 ; Munir MT2 ; Weng GJ3 ; Rhee KY4
Authors
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Authors Affiliations
  1. 1. Biomaterials and Tissue Engineering Research Group, Department of Interdisciplinary Technologies, Breast Cancer Research Center, Motamed Cancer Institute, Tehran, 1517964311, Iran
  2. 2. College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
  3. 3. Department of Mechanical and Aerospace Engineering, Rutgers University, New Brunswick, 08903, NJ, United States
  4. 4. Department of Mechanical Engineering (BK21 Four), College of Engineering, Kyung Hee University, Yongin, 446701, South Korea

Source: Applied Mathematics and Mechanics (English Edition) Published:2024


Abstract

In this study, the effects of stacked nanosheets and the surrounding interphase zone on the resistance of the contact region between nanosheets and the tunneling conductivity of samples are evaluated with developed equations superior to those previously reported. The contact resistance and nanocomposite conductivity are modeled by several influencing factors, including stack properties, interphase depth, tunneling size, and contact diameter. The developed model’s accuracy is verified through numerous experimental measurements. To further validate the models and establish correlations between parameters, the effects of all the variables on contact resistance and nanocomposite conductivity are analyzed. Notably, the contact resistance is primarily dependent on the polymer tunnel resistivity, contact area, and tunneling size. The dimensions of the graphene nanosheets significantly influence the conductivity, which ranges from 0 S/m to 90 S/m. An increased number of nanosheets in stacks and a larger gap between them enhance the nanocomposite’s conductivity. Furthermore, the thicker interphase and smaller tunneling size can lead to higher sample conductivity due to their optimistic effects on the percolation threshold and network efficacy. © Shanghai University 2024.
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