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Tailoring nanorod alignment in a polymer matrix by elongational flow under confinement: simulation, experiments, and
1School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA. ylj2@cornell.edu.
Soft Matter
|March 22, 2014
Summary
Controlling nanorod configuration in polymer fibers using extensional flow enhances sensing capabilities. Coaxial electrospinning improves nanorod alignment and signal strength for Raman spectroscopy applications.
Area of Science:
- Materials Science
- Polymer Science
- Nanotechnology
Background:
- Controlling nanorod dispersion and orientation in polymer matrices is crucial for advanced material properties.
- Elongational flow offers unique advantages over shear flow for aligning nanostructures.
Purpose of the Study:
- To investigate the influence of elongational flow and confinement on nanorod configuration in polymer melts.
- To optimize nanorod alignment in polymer nanofibers for enhanced sensing performance.
Main Methods:
- Coarse-grained molecular dynamics (CGMD) simulations of nanorod behavior in polymer melts under planar elongational flow.
- Electrospinning of gold (Au) nanorods within polyvinyl alcohol (PVA) matrices, including coaxial electrospinning.
- Transmission Electron Microscopy (TEM) for structural analysis.
- Surface-enhanced Raman spectroscopy (SERS) for sensing evaluation.
Main Results:
- Elongational flow promotes superior nanorod dispersion and orientation compared to shear flow.
- Simulations predicted and experiments confirmed that increasing nanorod aspect ratio and polymer chain length enhances alignment.
- Coaxial electrospinning successfully localized Au nanorods in the sheath layer, achieving better surface alignment.
- Au nanorod-PVA fibers produced via coaxial electrospinning exhibited significantly higher SERS signal intensity.
Conclusions:
- Extensional flow and coaxial electrospinning are effective strategies for directing nanorod orientation and dispersion in polymer fibers.
- Optimized nanorod configuration in polymer matrices leads to substantially improved sensing performance in SERS applications.

