Journal
PROGRESS IN POLYMER SCIENCE
Volume 35, Issue 12, Pages 1403-1419Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.progpolymsci.2010.08.002
Keywords
Polyaniline; Nanostructure; Nanotube; Self-assembly; Theory
Categories
Funding
- MacDiarmid Institute for Advanced Materials and Nanotechnology, Education New Zealand
- University of Auckland
Ask authors/readers for more resources
Nanostructured conducting polymeric materials are of exceptional interest due to their potential applications in sensors, actuators, transistors and displays. Arguably the most promising method for synthesizing polyaniline nanostructures is self-assembly, which is very advantageous in its simplicity and volume. However, this self-assembly remains only partly understood, with a number of already established models (a micelle theory and a phenazine theory) at odds with more recent discoveries (nanosheet curling and nanoparticle agglomeration), leading to a fragmented understanding of this important topic. In this paper we address this problem in two ways. First, we review the aforementioned older models and recent discoveries. Second, we propose an expanded polyaniline nanostructure self-assembly model - Multi-Layer Theory - that goes beyond the scope of existing theories, thereby accommodating the more recent discoveries. The expanded synthesis framework we present is based on a multi-layered approach incorporating intrinsic morphologies. The three proposed intrinsic morphologies underpinning our model are nanofibrils, nanosheets and nanoparticles; the forces driving their subsequent self-assembly interactions are mainly pi-pi stacking, hydrogen bonding and charge-charge repulsion from protonation. These interactions between the three intrinsic morphologies give rise to observed growth, agglomeration and curling behaviours that ultimately generate complex multi-layered nanostructures such as double-walled conducting polymer nanotubes. (C) 2010 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available