Numerical Studies of Thermal Conductivity in Functionalized Carbon Nanotubes

Although carbon nanotubes (CNTs) possess a large thermal conductivity, when they are incorporated in a polymer matrix their severe boundary resistance makes them ineffective at improving the thermal conductivity of the resulting composite. This resistance at CNT interfaces can be altered via chemical functionalization using mixed molecular chains to match the thermal impedance between CNTs and their environment. We explore the vast chemical space of possible configurations through meta-heuristics such as genetic algorithms and present candidate structures with optimal thermal conductance. We make comparisons of these CNT systems to certain harmonic lattices and look for important correlations among molecule parameters that contribute to the thermal conductivity, as is done in machine learning. We will summarize these results in general design rules for improving the thermal conductivity across molecular interfaces.