Some new developments in rubber reinforcement

The reinforcement of natural rubber provided by in-situ generated silica is compared with that obtained with anisotropic particles such as nanofibers of sepiolite. In addition, the effect of a dual loading is also investigated. Compared with networks filled with a single type of filler (sepiolite or in-situ generated silica), the samples containing two types of fillers display high modulus but less extensibility.     

Formation of graphitic carbon nanofiber (GCNF)/silica gel composites using surface-functionalized GCNFs and sol-gel processing

Reaction of herring-bone graphitic carbon nanofibers (GCNFs) containing surface-bound acid chloride functional groups with 3-aminopropyl triethoxysilane (APTES) leads to amide condensation and formation of carbon nanofibers surface-derivatized with pendant Si—OEt and Si—OH functional groups, GCNF-[C(O)NH(CH₂)₃Si(OEt)(OH)₂] _x . Addition of these GCNFs containing covalently bound 3-amidopropylsilyl linker molecules to silica sol-gel formulations gives GCNF/silica xerogels as dry black powders. Covalent binding of the linker molecule across the GCNF/ceramic interface is indicated by intermediate formation of especially stable GCNF/silica sol dispersions and the isolation of uniformly black GCNF/silica xerogel powders. SEM micrographs reveal excellent wetting of the carbon nanofiber surface by the silica xerogel matrix, and the presence of amido-carbonyl groups is confirmed from infrared spectral data. TGA plots show mass losses consistent with dehydration of the gel matrix and thermal decomposition of linker molecules at elevated temperature.     

Applications of the Graph Theory to the Prediction of Electrical and Dielectric Properties of Nano-filled Polymers

The addition of carbon nanofibers to a polymeric matrix is known to affect its mechanical and electrical properties, although the mechanisms responsible for the changes are not sufficiently understood. Particularly, there are currently no adequate predictive methods that allow the creation of knowledge-based structures tailored for specific electrical response. We have developed a method for predicting the electric and dielectric properties of nanofiber-reinforced polymer matrices based on the application of the graph theory and circuit laws. We consider the individual properties of the polymeric matrix and the complex nanofiber network (including fiber orientation, concentration, and size), under an applied external electric field, and from the analysis we obtain information such as perlocative pathways, breakdown voltage, and impedance of the overall system. Simulations for two-phase systems consisting of a dielectric matrix and randomly oriented nanofibers have shown that the concentration and the length of the fibers affect the properties. Increased concentrations or longer fibers both result in networks for which it is easier to establish conducting paths through breakdown mechanisms.