University of Wollongong

Abstract: Graphene sheets offer extraordinary electronic, thermal and mechanical properties and are expected to find a variety of applications. A prerequisite for exploiting most proposed applications for graphene is the availability of processable graphene sheets in large quantities. The direct dispersion of hydrophobic graphite or graphene sheets in water without the assistance of dispersing agents has generally been considered to be an insurmountable challenge. Here we report that chemically converted graphene sheets obtained from graphite can readily form stable aqueous colloids through electrostatic stabilization. This discovery has enabled us to develop a facile approach to large-scale production of aqueous graphene dispersions without the need for polymeric or surfactant stabilizers. Our findings make it possible to process graphene materials using low-cost solution processing techniques, opening up enormous opportunities to use this unique carbon nanostructure for many technological applications.

Abstract: Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

Abstract: The idea that a new generation of students is entering the education system has excited recent attention among educators and education commentators. Termed ‘digital natives’ or the ‘Net generation’, these young people are said to have been immersed in technology all their lives, imbuing them with sophisticated technical skills and learning preferences for which traditional education is unprepared. Grand claims are being made about the nature of this generational change and about the urgent necessity for educational reform in response. A sense of impending crisis pervades this debate. However, the actual situation is far from clear. In this paper, the authors draw on the fields of education and sociology to analyse the digital natives debate. The paper presents and questions the main claims made about digital natives and analyses the nature of the debate itself. We argue that rather than being empirically and theoretically informed, the debate can be likened to an academic form of a ‘moral panic’. We propose that a more measured and disinterested approach is now required to investigate ‘digital natives’ and their implications for education.

Abstract: Cognitive load theory suggests that effective instructional material facilitates learning by directing cognitive resources toward activities that are relevant to learning rather than toward preliminaries to learning. One example of ineffective instruction occurs if learners unnecessarily are required to mentally integrate disparate sources of mutually referring information such as separate text and diagrams. Such split-source information may generate a heavy cognitive load, because material must be mentally integrated before learning can commence. This article reports findings from six experiments testing the consequences of split-source and integrated information using electrical engineering and biology instructional materials. Experiment 1 was designed to compare conventional instructions with integrated instructions over a period of several months in an industrial training setting. The materials chosen were unintelligible without mental integration. Results favored integrated instructions throughout th...

Abstract: Micropollutants are emerging as a new challenge to the scientific community. This review provides a summary of the recent occurrence of micropollutants in the aquatic environment including sewage, surface water, groundwater and drinking water. The discharge of treated effluent from WWTPs is a major pathway for the introduction of micropollutants to surface water. WWTPs act as primary barriers against the spread of micropollutants. WWTP removal efficiency of the selected micropollutants in 14 countries/regions depicts compound-specific variation in removal, ranging from 12.5 to 100%. Advanced treatment processes, such as activated carbon adsorption, advanced oxidation processes, nanofiltration, reverse osmosis, and membrane bioreactors can achieve higher and more consistent micropollutant removal. However, regardless of what technology is employed, the removal of micropollutants depends on physico-chemical properties of micropollutants and treatment conditions. The evaluation of micropollutant removal from municipal wastewater should cover a series of aspects from sources to end uses. After the release of micropollutants, a better understanding and modeling of their fate in surface water is essential for effectively predicting their impacts on the receiving environment.