University of Wollongong

About: University of Wollongong is a education organization based out in Wollongong, New South Wales, Australia. It is known for research contribution in the topics: Population & Graphene. The organization has 15674 authors who have published 46658 publications receiving 1197471 citations. The organization is also known as: UOW & Wollongong University.

Buckled, stretchable polypyrrole electrodes for battery applications.

Abstract: Research on stretchable electronics is motivated by the need for electronic systems that can sustain large mechanical strain and still maintain their function. They can be wrapped conformally around complex and unconventional shapes, and have found applications in the biomedical fi elds, electronic paper display devices, sensor skins and photovoltaics. [ 1 ] Within the biomedical fi elds, these electronics need to conform to body shape for use as wearables with a match in mechanical properties to minimise discomfort. [ 2–5 ] As an indispensable component of stretchable electronics, a stretchable power-source device should also be able to accommodate large strain while retaining its function. Recently, there has been an emerging interest in stretchable power sources including energy storage [ 6–8 ] and energy harvesting. [ 9 , 10 ] Energy storage devices, including batteries and supercapacitors, play an important role in powering systems of portable electronic devices or implantable medical devices. Stretchable supercapacitor electrodes using single-walled carbon nanotubes (SWNT) integrated into poly(dimethylsiloxane) (PDMS) or textile substrates have been developed. [ 6 , 7 ] Ultracompliant electrochemical dry gel cells have been reported, comprising a cathode (MnO 2 ) and anode (Zn) paste plotted on a carbon black with structured elastomer as substrate. [ 8 ] To date, however, the application of inherently conducting polymers (ICPs) as stretchable supercapacitor or battery electrodes has not been reported. In particular, ICPs have been shown to be biocompatible with potential applications in biomedical implants, devices and tissue engineering. [ 11 , 12 ] This class of materials has attracted widespread attention in the applications in energy storage devices such as batteries or supercapacitors. [ 13 , 14 ] Here we report a stretchable battery electrode material based on buckled polypyrrole (PPy) and its application in a biocompatible battery system with a bioadsorbable Mg alloy (AZ61) in phosphate buffered saline (PBS). Remarkably, this PPy electrode demonstrates excellent stretchability. It can endure 2000 stretching cycles with 30% tensile strain applied at a 5% s − 1

Additive-free synthesis of 3D porous V2O5 hierarchical microspheres with enhanced lithium storage properties

Abstract: A facile synthesis of novel 3D porous V2O5 hierarchical microspheres has been developed, based on an additive-free solvothermal method and subsequent calcination. Due to their unique structure, these V2O5 microspheres display a very stable capacity retention of 130 mA h g−1 over 100 cycles at a current rate of 0.5 C, and show excellent rate capability with a capacity of 105 mA h g−1 even at the 30 C rate. The good electrochemical performance suggests that this unique hierarchical V2O5 material could be a promising candidate as a cathode material for lithium-ion batteries.

Incremental training of support vector machines

Abstract: We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.