Debundling and dispersion of carbon nanotubes (CNTs) in polymer solutions play a major role in the preparation of carbon nanofibers due to early effects on interfacial ordering and mechanical properties. First, we analyzed the propensity to achieve homogeneous dispersions of CNTs in polyacrylonitrile (PAN) and poly(methyl methacrylate) (PMMA) precursor solutions in the solvents DMSO, DMAc, and DMF. Molecular dynamics simulations at 25 and 75 °C with accurate interatomic potentials for graphitic materials that include virtual π electrons show tendencies for PMMA wrapping while PAN exhibits significant interactions only as the concentration is increased or solvent evaporated. Computed conformations, solubility, and temperature dependences correlate well with experimental data from spectroscopy, light scattering, and viscosity measurements. Subsequent heating and drawing of the precursor gels results in PAN/CNT composites. MD simulations reveal the structure at the atomic scale and relationships to mechanical and thermal properties for different degrees of polymer crystallinity and CNT diameter. Glass transition temperatures correlate with the amount of CNT/polymer interfacial area per unit volume and were predictable with +/-5 K accuracy relative to experiments. Tg increases for higher CNT volume fraction and inversely with CNT diameter. An important aspect is the knowledge of CNT bundle size from experiment, which affects the effective surface area. Computed interfacial shear strengths increase when PAN has at least some degree of crystallinity (at least 50% crystalline). Third, testing of mechanical properties up to failure has been performed for a wide range of over 500 CNT and defective CNT morphologies to explore the role of structural defects and new information from machine learning models. Hereby, we utilize a new force field, IFF-R, that enables bond breaking in high accuracy and computational speed. Suitable feature representations and processing of the data through reinforcement learning and Bayesian uncertainty quantification will be discussed, and the resulting correlations between structure, Young’s modulus, and tensile strength. Ongoing questions include the application of supervised learning approaches to polymer composites that have more degrees of freedom and to experimental data from tomography and X-ray diffraction that will become available in large quantities. Progress and opportunities will be discussed.

Understanding CNT/polymer interphase assembly and mechanical strength using simulation and reinforcement learning

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.