Computationally Modeling Strenthening Mechanismsin Carbon Nanotube Composites and Bundles

[摘要] Carbon nanotubes (CNT) have extraordinary mechanical properties, but totake advantage of these properties in composites, bundles, and ropes requiresstrong bonding to achieve significant CNT-CNT or CNT-matrix load transfer. Thiswork is a computational study examining strengthening CNT composites andbundles on the quantum, atomistic, and meso- scales. Density functional theory(DFT) and classical molecular dynamics (MD) are used to evaluate methods toimprove bonding CNT-matrix crosslinking by the inclusion of dopants, defects,functional groups, and curvature. DFT and MD are also used to quantify the loadtransfer of CNT-CNT sulfur crosslinks. A coarse-grained (CG) technique for modelingCNTs on the mesoscale is extended to include nonconservative frictional forceswhich are parameterized to model crosslinking. This extended CG model is thenused to predict the mechanical performance of CNT bundles on a much larger scale.In addition to utilizing these traditional computational material science methods, ageneral approach is developed for applying machine learning (ML) to predict theground state electron and energy density of an atomistic system.

[发布机构] Rice University

[效力级别] Nanotubes