Abstract
ABSTRACTMetallic nickel nanowires with excellent physical properties have been introduced into polydimethylsiloxane matrix to form polymer nanocomposites. Nanowires were synthesized by template-assisted electrochemical deposition. By utilizing ferromagnetic nickel nanowires, small external magnetic field can be used to control their alignment and distribution during composite synthesis. Unlike dielectrophoresis, optical tweezers, and microfluidic flow control, magnetic manipulation provides a cost-effective, non-contact, and versatile approach to control nanostructured materials in fluids over a large area. Polydimethylsiloxane composites with nanowires arranged in longitudinal, transverse, and random orientations with respect to the applied load direction were studied. Tensile tests showed that the composites with longitudinal arrangement have higher elastic modulus and tensile strength than the other composite samples. Experimentally obtained elastic modulus values were compared with the prediction of classical Halpin-Tsai model. Metallic nickel nanowires with excellent physical properties have been introduced into polydimethylsiloxane matrix to form polymer nanocomposites. Nanowires were synthesized by template-assisted electrochemical deposition. By utilizing ferromagnetic nickel nanowires, small external magnetic field can be used to control their alignment and distribution during composite synthesis. Unlike dielectrophoresis, optical tweezers, and microfluidic flow control, magnetic manipulation provides a cost-effective, non-contact, and versatile approach to control nanostructured materials in fluids over a large area. Polydimethylsiloxane composites with nanowires arranged in longitudinal, transverse, and random orientations with respect to the applied load direction were studied. Tensile tests showed that the composites with longitudinal arrangement have higher elastic modulus and tensile strength than the other composite samples. Experimentally obtained elastic modulus values were compared with the prediction of classical Halpin-Tsai model.
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