Optimally conductive networks in randomly dispersed CNT:graphene hybrids

Area of Science:

• Materials Science
• Nanotechnology
• Electrical Engineering

Background:

• Carbon nanotubes (CNTs) and graphene are promising nanomaterials for conductive applications.
• Hybrid materials combining CNTs and graphene offer potential for enhanced properties.
• Understanding the relationship between structure and conductivity in these hybrids is crucial.

Purpose of the Study:

• To develop a predictive model for the synergistic electrical conductivity of CNT:graphene hybrids.
• To quantitatively describe the role of inter-particle contacts (CNT-CNT, graphene-graphene, CNT-graphene) in hybrid conductivity.
• To correlate theoretical predictions with experimental findings.

Main Methods:

• Development of a predictive model based on random particle distribution and an orientation density function.
• Calculation of the number of various inter-particle contacts within the hybrid structure.
• Fabrication of CNT:graphene hybrids using inkjet printing for experimental validation.

Main Results:

• The total number of particle contacts peaks at a specific composition ratio, influenced by particle sizes.
• CNT:graphene hybrids exhibit higher electrical conductivities than pure CNT or graphene materials at certain ratios.
• Experimental results confirm the model's predictions regarding composition-dependent conductivity.

Conclusions:

• The proposed predictive model accurately describes the synergistic electrical conductivity of CNT:graphene hybrids.
• The number and type of inter-particle contacts are critical factors determining the overall conductivity.
• This contact-based approach is effective for investigating the properties of particulate hybrid materials.