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Tag: Graphene
  • Exploration of Two Polymer Nanocomposite Structure-Property Relationships Facilitated by Molecular Dynamics Simulation and Multiscale Modeling

    Abstract: Polyamide 6 (PA6) is a semi-crystalline thermoplastic used in many engineering applications due to good strength, stiffness, mechanical damping, wear/abrasion resistance, and excellent performance-to-cost ratio. In this report, two structure-property relationships were explored. First, carbon nanotubes (CNT) and graphene (G) were used as reinforcement molecules in simulated and experimentally prepared PA6 matrices to improve the overall mechanical properties. Molecular dynamics (MD) simulations with INTERFACE and reactive INTERFACE force fields (IFF and IFF-R) were used to predict bulk and Young's moduli of amorphous PA6-CNT/G nanocomposites as a function of CNT/G loading. The predicted values of Young's modulus agree moderately well with the experimental values. Second, the effect of crystallinity and crystal form (α/γ) on mechanical properties of semi-crystalline PA6 was investigated via a multiscale simulation approach. The National Aeronautics and Space Administration, Glenn Research Center's micromechanics software was used to facilitate the multiscale modeling. The inputs to the multiscale model were the elastic moduli of amorphous PA6 as predicted via MD and calculated stiffness matrices from the literature of the PA6 α and γ crystal forms. The predicted Young's and shear moduli compared well with experiment.
  • Method Selection Framework for the Quantitation of Nanocarbon Scientific Operating Procedure Series (SOP-C-3): Selection of Methods for Release Testing and Quantitation of Solids, Suspensions, and Air Samples for Carbon-Based Nanomaterials

    Abstract: There is significant concern regarding the health and safety risk of nanocarbon (for example, nanotubes, graphene, fullerene), and the cur-rent capability gap for accurately determining exposure levels encumbers risk assessment, regulatory decisions, and commercialization. Given the various analytical challenges associated with the detection and quantitation of nanocarbon, it is unlikely that a single method or technique will prove effective for all forms of nanocarbon, all exposure scenarios, or all possible environmental systems. The optimal approach, or series of techniques, will likely depend on the nature of the material being measured, its concentration, and the matrix in which it is contained. In this work, a preliminary decision framework is presented that assists the user in deter-mining which analytical methods are best suited for a given sample.