Vacation Research Scholarships: Information Technology, Engineering and the Environment

These scholarships give you the opportunity to earn $300 a week undertaking research for up to 8 weeks with experienced researchers, usually between November and February, in a recognised research institute or centre within the University.  Applications close 17 September 2017.

Click on the discipline areas below to view the different Vacation Research projects available in the Division of Information Technology, Engineering and the Environment for 2017-18.  

Projects

Further information

For more information about the scholarships and how to apply click here.

Vacation Research Project Descriptions

Information Technology & Mathematical Sciences

Project Title
Project description

Forward and inverse electromagnetic scattering

Project keyword(s): Applied Mathematics

When a radio wave meets a dense body, the irregular density of the body contributes to scatter the radio wave in different directions. In forward scattering we try to predict the wave spread from our knowledge of the body density. In inverse scattering we try to discover the body density from information about the wave spread. The work involves knowledge of differential equations.

Contact: Dr Jorge Aarao | E Jorge.Aarao@unisa.edu.au | T 8302 3741 | URL  http://people.unisa.edu.au/Jorge.Aarao

Singular perturbations in differential equations

Project keyword(s): Applied Mathematics

The goal is to study the behaviour of solutions to a differential equation, when the equation itself depends on a parameter. The case of interest is when the parameter vanishes, and makes the higher derivatives disappear from the equation. Many examples are known, but not a full general theory.

Contact: Dr Jorge Aarao | E Jorge.Aarao@unisa.edu.au | T 8302 3741 | URL  http://people.unisa.edu.au/Jorge.Aarao

Comparative Evaluation of Natural Language Parsers

Project keyword(s): Natural Language Processing, Software Engineering, Evaluation

Recently, Google opened-sourced their Natural Language Parser and many groups promoted or released their own in response, providing a slew of state-of-the-art parsers for academics and organisations to use in their work. At the same time, there are many commonly used parsers, such as the Stanford Parser, that are widely considered to provide good results. However, there is no high-quality, academically rigorous, and comparable evaluation of these parsers: old or new. This project will produce a comparative evaluation of the currently available Natural Language Parsers, including both older parsers and those newly released. The project includes the design and execution of a systematic evaluation performed on common test corpora and will form the basis for the comparison of the Natural Language Understanding engine being developed within the Knowledge and Software Engineering Laboratory. 

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Inferring Semantic Relations from Word Co-occurrence Vectors

Project keyword(s): Knowledge Acquisition, Machine Learning

A common technique in Natural Language Processing, popularised by Google’s word2vec, is to analyse a corpus of text to produce co-occurrence vectors for each word (basically the vectors contain one dimension per word pair), which enables us to determine the similarity between two words. Such vector models, or distributional semantics, demonstrates some neat properties: for example, subtracting the vector for 'man' from the vector for 'king' and adding the vector for 'woman' results in a vector very close to that of 'queen'. However, such models are limited, finding most use in indexing and information retrieval (such as Google's search engine), tend to focus on nouns, and are not truly semantic. For example, two word vectors may be close together, not because they are similar concepts, but because they are strongly related through some (unknown) relationship. These relationships are often identifiable by the verbs and prepositions used between them. This project aims to be a starting point for inferring semantic relations between word co-occurrence vectors by incorporating the co-occurrence information of prepositions and verbs. The candidate will develop an initial prototype that will identify basic semantic relations (such as parthood, and more general/more specific terms) from key prepositions as well as a small set of domain specific relations of interest.

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Unstructured Information Management Implementation 

Project keyword(s): Software Engineering, Natural Language Processing

Modern text analysis applications require many different tools (existing and novel) to work together across multiple platforms, programming languages, and architectures. These tools must work in parallel or sequential pipelines that can be configured and customised by the application developer. Therefore, standardised execution architectures, such as the UIMA (Unstructured Information Management Architecture) standard released by OASIS, are a necessity for ensuring the interoperability and consistent execution of different tools combined in an application. To this end, we are looking for a student to develop a standards compliant implementation of UIMA in the Smalltalk programming language and environment, along with compatible implementations of core language processing tasks (e.g. tokenisation and sentence splitting). If time permits, the student would also develop adapters, using the Smalltalk-Java Bridge ‘JNIPort’, to the Java implementation, Apache UIMA, which is not 100% UIMA compliant.

Contact: Professor Markus Stumptner, Dr Matt Selway | E Markus.Stumptner@unisa.edu.au | T 8302 3965 | URL http://people.unisa.edu.au/Markus.Stumptner

Deep neural networks for human emotion recognition 

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

Newly developed "deep learning" methods have reignited the field of neural networks in the last few years. For example, Google DeepMind recently announced the first computer program that plays the game of Go at human expert level, and this relied on deep learning.  The aim of this project is to design software that learns to robustly recognise human emotions, by making use of multiple types of sensor data, such as video, still images, and biometrics. It is expected that the main algorithm to be implemented will be a deep convolutional neural network. It will be based on the earlier work of Yu and Zhang [1] who have been able to get emotion recognition rates of up to 85% with a neural network technique.  The context of use will be explore if emotional recognition code can be developed that can run in near real time on live camera video and so provide feedback on user emotion while operating a computer interface. For example, using the video feel from a laptop camera to monitor the emotions of a person in front of it

Contact: Professor Mark Billinghurst, Associate Professor Mark McDonnell | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

Face to Face Collaboration Using HoloLens

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

The Microsoft HoloLens hardware combines a see-through head mounted display with excellent indoor tracking, and so provides an ideal platform for Augmented Reality. In this project we want to explore how the HoloLens could be used to enhance face-to-face collaboration.

The project will involve developing an example HoloLens application that will allow two people in the same room to view and interact with the same virtual content. This will build on earlier work that we have done in face-to-face AR interaction [1][2]. In addition we will explore novel interaction methods such as using virtual cues to show where people are looking, and enabling users to see from each other viewpoints.

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst

Gaze based remote conferencing

Project keyword(s): Computer Science, Wearable Computing, Teleconferencing

For a number of years people have been studying how head worn cameras (HWCs) and head mounted displays (HMDs) can be used for remote collaboration on physical tasks. The HWC allows a remote expert to see what the local user is doing, while a HMD can allow the remote expert to provide Augmented Reality (AR) virtual cues overlaid on the local user’s view of the real world to help them complete the task. For example, in a remote maintenance task, workers using a wearable AR interface were able to reduce their task performance time by up to 30% [1].

In face-to-face conversation gaze provides information about where a person is directing his or her attention and so it could also be an important cue in remote collaboration. Previous research has found that sharing gaze between two remote collaborators significantly improved performance on a desktop visual search task, compared to audio only communication [2]. However there has been little research conducted on sharing gaze cues from a wearable collaborative system. In this project we want to explore the effect of adding gaze tracking to wearable systems for remote collaboration. 

The work would extend our earlier pilot work in this area [3] and involve the following: Background research on gaze tracking in collaborative systems, Create a prototype system integrating a HMD, HMC and eye-tracker, Conduct user studies with a variety of physical tasks, and Write research report.

Contact: Professor Mark Billinghurst | E Mark.Billinghurst@unisa.edu.au | T 8302 3747 | URL http://people.unisa.edu.au/Mark.Billinghurst    

MIMO and modulations in visible light communications

Project keyword(s): Free space optical communications

To efficiently use the radio frequency (RF) spectrum is an important area due to the scarcity of the limited spectrum bandwidth. Despite the efforts to improve the RF spectrum efficiency, the data throughput demand has outpaced the development. Optical wireless communication has shown the potential to bridge the demand gap due to the wide bandwidth availability of the optical spectrum. The unregulated and easily available bandwidth (over terahertz) provided by Visible Light Communication (VLC) is one of the main factors that gives these systems an advantage over the existing radio frequency (RF) communications. To use visible light for data communications has gained much attention recently and is developing as a viable and beneficial communication technology, especially for short-range indoor systems. The advent of high-power light emitting diodes (LEDs) and highly sensitive photo diodes (PDs) and simultaneous use as a source of lighting and data communication have helped the development of VLC as an attractive as well as energy-efficient technique for high-speed data communication. This project will investigate novel approaches to deal with new challenges in VLC, including coding techniques for intensity constraints in VLC systems, single carrier and multi-carrier modulations, etc. In particular, a Multiple-Input Multiple-Output (MIMO) system will be developed by using multiple PDs and LEDs.

Contact: Dr Siu Wai Ho | E SiuWai@unisa.edu.au | T 8302 3858 | URL http://people.unisa.edu.au/SiuWai.Ho

Positioning by visible light communications

Project keyword(s): Free space optical communications

Location-based services are becoming increasingly important. By knowing a user’s physical location, a mobile device can provide adequate information to the user and support different mobile applications. For example, we can have navigation applications and tracking/monitoring applications in our smartphone. To locate a user in outdoor environments, Global Positioning System (GPS) and cellular-based positioning have been widely used. For indoor environments, the performance of these systems is degraded because signals are blocked by walls or infrastructure. Therefore, alternative solutions are needed for indoor positioning. For example, positioning systems can be built over wireless local area networks (WLANs). However, the accuracy of these systems depends on whether the indoor environment is complicated or not. The accuracy can be from one up to several meters.

This project will investigate an indoor positioning system which is cost-effective and provides accuracy levels within 0.2 meters by using Visible Light Communications (VLC), which is an emerging and promising research area. The aim of this project is to develop algorithm and system designs for such a high precision requirement.

Contact: Dr Siu Wai Ho | E SiuWai@unisa.edu.au | T 8302 3858 | URL http://people.unisa.edu.au/SiuWai.Ho

Remote Internet of Things Powered by Energy Harvesting and Wireless Power Transfer

Wireless sensor and actuator networks (WSAN) consisting of hundreds or thousands of nodes are envisaged to support tether-free embedded sensing and control applications for Internet of Things (IoT) over a long period of time, typically 20-30 years. To date, traditional grid-powered and non-rechargeable battery powered devices have been primarily used to power such networks and the corresponding engineering design goal has been to either minimize power/energy consumption for a particular sensing/control task or to maximize the operational lifetime of the network for a fixed amount of energy resource. In this setting, optimal resource allocation for energy-limited wireless sensor networks (WSN) has been investigated largely in the context of information gathering. Energy-efficient estimation and control algorithms have been also investigated primarily for WSANs powered by non-rechargeable batteries. In contrast to non-rechargeable battery powered devices, energy harvesting based rechargeable batteries or energy storage devices offer several significant advantages in deploying large scale WSANs. These devices, when integrated into the sensor/actuator nodes, offer the freedom from the cost-prohibitive task of periodically replacing batteries in hundreds/thousands of nodes, and the possibility to operate in a virtually everlasting manner, along with reducing their carbon footprint. Widespread adoption of such energy harvesting devices based on solar/wind/electromagnetic/vibrational energy are being considered for large scale WSANs in applications such as home and building automation, industrial manufacturing, and structural health monitoring. In addition, a recent breakthrough in the area of wireless power transfer technology based on coupled magnetic resonance, has also opened up a new energy management paradigm in WSNs. While wireless power transfer is still at its infancy, there have been some recent encouraging experimental results using various modes of wireless power transfer.

The focus of this project is on designing and analyzing jointly optimized signal processing, communication and control algorithms together with energy management algorithms for wireless sensor networks powered by energy harvesting and wireless power transfer. Possible experimental set-ups may also be developed with commercially available hardware and software.

Contact: Dr Subhrakanti Dey | T +61 8 8302 3863 | E Subhra.Dey@unisa.edu.au | URL http://people.unisa.edu.au/Subhra.Dey

Identifying cancer subtypes from multi-levelled biological data with computational methods

Project keyword(s): Computer Science, Bioinformatics

Cancer is a leading cause of death, accounting for more than 8.2 million of deaths worldwide, or 22,000 people every day. In the past decade, personalised medicine, using genetic information to develop cancer-specific medication, has become a strong focus for health researchers. An important step in this personalised medicine framework is to identify cancer subtypes, as different cancer subtypes may have different treatment therapies. Since cancer is an extremely complex and heterogeneous disease, the personalised medicine framework relies heavily on achievements of advanced research in system biology (Wang, 2010).  System biology approaches use knowledge in Mathematics, Statistics and Computer Science to solve the biological problems. This project will study the computational methods for identifying cancer subtypes using multi types of biological data. Examples of related works are in (Wang et al. 2014, Liu et al. 2014). Background in Biology is an advantage but not a compulsory requirement.

Contact: Dr Thuc Le | T (08) 830 23996 | E Thuc.Le@unisa.edu.au | URL http://people.unisa.edu.au/Thuc.Le

Professor Jiuyong Li | T (08) 830 23898 | E Jiuyong.Li@unisa.edu.au | URL http://people.unisa.edu.au/Jiuyong.Li 

Investigating genetic causes of cancer through complex gene regulatory networks

 

Project keyword(s): Computer Science, Bioinformatics

This project will study the computational methods for identifying the genetic causes of cancer through gene regulatory networks containing multiple gene regulators. Gene regulatory networks play an important role in every process of life, and understanding the dynamics of these networks helps reveal the mechanisms of diseases6. There have been tremendous works on inferring gene regulatory networks. However, most of the works consider the networks with only one type of gene regulator, such as transcription factors (Imam et al., 2015) or microRNAs (Le, 2013), thus only help reveal part of the whole regulatory network picture. This project aims to develop methods to construct gene regulatory networks that contain multiple types of gene regulators and methods to isolate sub-networks that are altered between normal and cancer patients. Examples of related works are in (Le et al. 2013, Ping et al. 2015). Background in Biology is an advantage but not a compulsory requirement.

Contact: Dr Thuc Le | T (08) 830 23996 | E Thuc.Le@unisa.edu.au | URL http://people.unisa.edu.au/Thuc.Le

Professor Jiuyong Li | T (08) 830 23898 | E Jiuyong.Li@unisa.edu.au | URL http://people.unisa.edu.au/Jiuyong.Li 

Integrated Prediction with Multiple Data Sources and Credibility Assessment

 
Project keyword(s): Computer Science, Data Mining

The research topic will focus on the problems of fusing evidence from multiple data sources and models, and the credibility of data sources and users.  The topic will be based on the work in Rekatsinas et al. (2015)’s paper on the challenge of discovering valuable sources, Hoegh et al. (2015)’s paper, in which a Bayesian model fusion framework of protest events is proposed, and the work in (Mukherjee, Weikum and Danescu-Niculescu-Mizil 2014).

Contact:  Dr Lin Liu | T (08) 830 23311 | E Lin.Liu@unisa.edu.au | URL http://people.unisa.edu.au/Lin.Liu

Relationship of Dengue Infections in Different Cities in Twitter Data

Project keyword(s): Computer Science

This project aims to model the relationship between dengue outbreaks in different Australia cities or/and in cities of a neighboring country like Malaysia. Twitter data and government disease statistics will be used in prediction. The model is then used to predict the outbreak of a specific city based on the infections of other cities. This model will be useful if it is adapted to cities across cities in different countries.

Contact: Dr Jixue (Jerry) Liu | T (08) (08) 830 23054 | E Jixue.Liu@unisa.edu.au | URL http://people.unisa.edu.au/Jixue.Liu

Developing novel data mining techniques for mining educational data

Project keyword(s): Computer Science, Data Mining

This project aims to develop data mining techniques for effectively identifying the factors that influence student academic performance and building better models to predict student learning outcomes. The increasing adoption of learning management systems, such as Moodle has enabled education institutions to collect a large of amount of data related to student online activities. Findings from such data can assist the institutions to provide timely and effective student support and to make interventions. Educational data mining [1] has been attracting more and more research interests in recent years. However, due to the large volume and high complexity of the data logged by the learning management systems, traditional data mining methods are facing new challenges to deal with the big educational data to find out true influential factors on student performance and to build accurate and interpretable models to predict student outcomes. This project will develop new methods, such causal discovery approaches [2] to tackle the educational data mining challenges.

Contact:  Dr Lin Liu | T (08) 830 23311 | E Lin.Liu@unisa.edu.au | URL http://people.unisa.edu.au/Lin.Liu

Professor Jiuyong Li | T (08) 830 23898 | E Jiuyong.Li@unisa.edu.au | URL http://people.unisa.edu.au/Jiuyong.Li 

School of Health Sciences collaborations

Can physical activity help to prevent cognitive decline or depression? : gene-environment interaction study

Centre/Institute:  CPHR (SAHMRI campus) , Sansom Institute

Project keyword(s): Physical activity, genes, cognitive function, obesity

This is an exciting opportunity for a high performing student to join the Nutritional and Genetic Epidemiology group based at the SAHMRI campus. The student will need to be research orientated, and interested in developing strong skills in statistical analyses and research reporting. The supervisory team will consist of Prof Elina Hypponen, Dr Ang Zhou and Mr Anwar Mulugueta (PhD student). It is expected that the project will lead to publication of a research paper in a rebuttable journal which will be co-authored by the student (depending on level of contribution, possibly led by the student).

Genes influence our health, but genes are not our destiny. This project will work to establish whether higher levels of physical activity can help to overcome genetically determined increases in disease risk.  Based on the interests of the student, there are two separate focus areas to choose from and the overall aim will be either to establish if physical activity can help to overcome the adverse effects of genetically determined obesity risk on 1) cognitive function or 2) depression/anxiety.  The project will be based on the UK Biobank with over 500,000 participants, and benefit from earlier work done on data cleaning and management.  Student will be expected to conduct literature reviews,  to conduct statistical analyses (closely supported and advised by the NGE team), and to prepare a full manuscript draft, complying with standards of high quality research reporting.   

Contact: Professor Elina Hypponen | T +61 8 8302 2518 | E Elina.hypponen@unisa.edu.au

Dr Ang Zhou | T + 61 8 830 20286 | E ang.zhou@unisa.edu.au

Mr Anwar Mulugeta | +61405080311 | E gebam006@mymail.unisa.edu.au

Modifiable lifestyle factors, genetic susceptibility and risk for hypertension: a gene-environment interaction study

Centre/Institute:  CPHR (SAHMRI campus) , Sansom Institute

Project Keyword(s): Lifestyle factors, blood pressure, genes, interactions

Blood pressure (BP) is a vital part of how individual’s heart and circulation works and persistently high BP is one of the main risk factors for heart disease. There is a genetic component in BP regulation, with some more susceptible to high BP and adverse cardiovascular events. Lessons from gene-environment interaction studies suggest that adverse genetic effects can in some cases be alleviated by altering our behaviours. A range of lifestyle factors, such as physical activity, obesity, coffee intake, smoking, and alcohol intake are known to influence BP. However, we do not know whether altering those lifestyle factors can also help to overcome some of the adverse genetic effects on BP. Using the UK Biobank study, one of the largest population-based studies of this kind (~ 500,000 participants), this project will examine lifestyles factors influencing BP, genetic risk score for BP and their joint effects on BP. The results from this study will help to determine what and how lifestyle factors can alleviate individual’s genetic susceptibility to high BP, providing first-stage evidence base for individually tailored lifestyle recommendations.

This project will be suited for enthusiastic students who have an interest in deepening their knowledge in genetics and epidemiology research. It will be particularly well suited for a well-performing student who consider progressing in their studies all the way to a PhD in genetic epidemiology/statistical genetics.

Contact: Professor Elina Hypponen | T +61 8 8302 2518 | E Elina.hypponen@unisa.edu.au

Dr Ang Zhou | T + 61 8 830 20286 | E ang.zhou@unisa.edu.au

Mr Anwar Mulugeta | +61405080311 | E gebam006@mymail.unisa.edu.au

Obesity, genetic susceptibility and depression risk: a gene-environment interaction study  

Centre/Institute:  CPHR (SAHMRI campus) , Sansom Institute

Project Keyword(s): Obesity, depression, genes, interaction 

Obesity and depression are both global public health problems. The exact causes of these disorders are not yet clearly understood. There is a genetic component, with heritability estimates varying between 40 to 70% for obesity and 40 to 50% of depression. Modifiable environmental factors also affect depression risk, and among others, obesity is one of the proposed risk factors. However, little is still known about the joint effects between genes and environment, or how genetic susceptibility to obesity affects the association between obesity and depression risk. In this project, the student will investigate whether the association between obesity related genetic variants and depression is different among obese and non-obese individuals of African-ancestry living in the UK.  This project will contribute to larger to cross-ethnic analysis, and complement work we are currently undertaking in a Caucasian ancestry population. Data will be derived from the UK Biobank, which is one of the largest population-based studies of this kind (~ 500,000 participants).

This project will be suited for enthusiastic students who have an interest in deepening their knowledge in genetics and epidemiology research. It will be particularly well suited for a well-performing student who consider progressing in their studies all the way to a PhD in genetic epidemiology/statistical genetics.

Contact: Professor Elina Hypponen | T +61 8 8302 2518 | E Elina.hypponen@unisa.edu.au

Dr Ang Zhou | T + 61 8 830 20286 | E ang.zhou@unisa.edu.au

Mr Anwar Mulugeta | +61405080311 | E gebam006@mymail.unisa.edu.au

 

 

Engineering

Project title
Project description

Real-time ion sensing, using conducting polymers

Centre/Institute: Future Industries Institute

Project keyword(s): Sensors, Conducting Polymers, Electronics, Manufacturing

Conducting polymers are an interesting class of materials that bring the electrical properties of metals together with the mechanical properties of polymers.  More recently they have shown promise to do new things, such as selective interaction with certain salts and ions in water.  When this selectivity is combined with a monitoring process, a novel range of sensors can be developed.  This project will explore ways to tune the selectivity, investigate the electrical or optical techniques to detect the polymer response, and importantly develop processes to manufacture the materials and device.  The overall aim is to enhance the manufacturability and performance of conducting polymers so our industry partners can exploit them in new commercial products.

Contact: A/Prof Drew Evans | T 83025719 | E drew.evans@unisa.edu.au

Natural and Built Environments

Project title
Project description

Distribution and bioaccessibility of heavy metals in household carpet dust.

Centre/Institute: Future Industries Institute

Project keyword(s): environmental contamination, heavy metals, house dust, human health, lead.

Household carpets can act as both a source and sink for household dust and any dust associated environmental contaminants. Dust enters residential homes either externally, typically as airborne dust or via tracking of particulate matter on shoes, or internally from dust generating activities carried out within the house. Dust can be a significant route of respiratory exposure to adults as well as via the gastrointestinal route to young infants who are often in close proximity to floor surfaces and exhibit hand-to-mouth behaviour. Since fine particular matter often sticks to children’s hands, hand-to-mouth behaviour is a significant route for childhood ingestion of heavy metals. Thus accurate measurement of metal concentrations in household dust is important as lead (Pb) and other industrial metals represent a potential health hazard to children and their development.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Distribution and speciation of metals in traditional medicinal plants.

Centre/Institute: Future Industries Institute

Project keyword(s): environmental risk, heavy metals, medicinal plants, phytoavailability, plant uptake.

In many countries worldwide, traditional medicine using locally grown plants continues to be a common practice. However, there is a growing concern that this practice can potentially lead to detrimental health effects related to heavy metal toxicity when medicinal plants are consumed which accumulate high levels of heavy metals. The aim of this project is to determine the magnitude and severity of heavy metal contamination in medicinal plants and to understand the soil-to-plant transfer patterns of these heavy metals for a number of common medicinal plants. This study could be applied to medicinal plants from India or traditional Chinese medicines, or indeed any cultural background that has a tradition of medicinal plant use.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Are biodegradable plastic bags an environmental risk?

Centre/Institute: Future Industries Institute

Project keyword(s): biodegradation, composting, environmental risk, heavy metals, plastic bags.

Biodegradable plastic bags are becoming increasingly attractive in Australia because of an associated “green” image. However, residual metals present in many biodegradable plastic bags may potentially pose a long-term threat to the environment. This projects aims to provide baseline information on the type and potential for environmental harm from existing biodegradable plastic bags used in Australia and will assess the toxicological effect on the environment associated with the adoption of biodegradable plastics shopping bags when disposed to landfill or following composting.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Speciation and severity of Arsenic content in Australian rice

Centre/Institute: Future Industries Institute

Project keyword(s): arsenic, environmental risk, food security, speciation, rice.

Arsenic (As) is a colourless and odourless poison where long-term sub-lethal exposure is associated with increased human health risk and the development of various cancers. In many South East Asian countries and the USA rice is often found to contain elevated levels of As. In Asia these elevated levels are commonly associated with irrigation of rice with As contaminated groundwater, while in the USA such levels are more commonly associated with the ubiquitous use of As based pesticides. In Australia very little is known about the source or extent of As content of rice. In this project the distribution and severity of As in rice commercially available in Australian markets and/or grown in Australia will be determined and the risk of human health effects quantified.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Cadmium accumulation in Perilla grown on contaminated soils

Centre/Institute: Future Industries Institute

Project keyword(s): cadmium, environmental risk, food, Perilla, soil.

Perilla frutescens is a member of the mint family widely used throughout South East Asia in both cooking and as a traditional medical herb. Of potential concern is that the exact same plant species is also identified as an efficient hyperaccumulator of the toxic heavy metal Cadmium (Cd). Thus there is concern that Perilla cropped on contaminated soils may accumulate metals in its edible parts and thus pose a potential human health risk. In this study, as part of an ongoing Masters project, Perilla will be cropped on a Cd contaminated soil and the magnitude and bioaccessibility of Cd accumulated in edible plant parts assessed.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Green synthesis of metal oxide nano-adsorbents for waste water treatment 

Centre/Institute: Future Industries Institute

Project keyword(s): green synthesis, nanomaterials, plant extracts, pollution, water treatment.

Worldwide, the excessive consumption of water, compounded with a general deterioration in the quality of both surface and ground waters due to anthropogenic activities such as agriculture, is a major threat to water security and ecosystem health. Consequently there is a need to develop cost effective and efficient adsorbents for a variety of anionic pollutants. While engineered nanomaterials (ENMs) are an emerging class of potential adsorbents their manufacture often involves toxic chemicals and solvents. Recently green synthesis of ENMs using simple plant extracts has been proposed as a safer synthetic route. In this project a metal oxide nanomaterial will be prepared via a green synthetic route and characterised for its removal efficiency or one or more common polluting anions (i.e. arsenate, chromium oxyanions, nitrate or phosphate)

Contact: Durr-e-Shahwar Noman | T + 61 8 8302 3591 | E durr-e-shahwar.noman@mymail.unisa.edu.au

Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Development of polymer nanocomposite super-adsorbents for wastewater treatment.

Centre/Institute: Future Industries Institute

Project keyword(s): clay, graphene oxide, nanocomposite, polymer, water treatment

While both clays and graphene oxide have been widely proposed as suitable nanomaterials for contaminant removal from aqueous solutions they often suffer from poor flow properties limiting their practical application for water treatment. This project investigates the potential of combining the good pollutant adsorption properties of two nanomaterials (natural clays and graphene oxide) with the good flow properties of synthetic polymers. We propose that poor flow characteristics of the adsorbents can be alleviated via their immobilisation within a permeable polymer support to form a nanocomposite with enhanced pollutant binding properties and high flow characteristics. The major outcome will be new super-adsorbent materials suitable for the removal of heavy metal(loid)s from wastewater.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Low cost efficient adsorbents for dye decolourization of textile waste effluents.

Centre/Institute: Future Industries Institute

Project keyword(s): adsorbents, azo dyes, surface modification, textile effluent, water treatment.

Industrial pollution of water continues to be of global issue; particularly in developing countries where limited water resources are already stretched to breaking point and governments lack the resources to implement full wastewater treatment. In particular, textile and tanning industries, which are prevalent in many developing countries, have considerable issues with treating their effluent streams and a range of cheap adsorbents are required. To address this important issue this project will evaluate the potential of low cost surface functionalized sands for wastewater treatment of textile effluents.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Surface modified rice husk derived biochar as a cost effective adsorbent for water treatment?

Centre/Institute: Future Industries Institute

Project keyword(s): biochar, heavy metal, rice husk, surface modification, water treatment.

In many developing countries access to clean drinking water is not always guaranteed due to the high costs associated with water treatment. Thus access to cheap but efficient adsorbents for water purification is becoming essential. Rice hull derived biochar may be an option here because in many developing countries, where rice is commonly cropped as a staple food, large amounts of rice husk are also produced as a waste biomass, which is often simply burnt in the fields leading directly to air contamination. This project tests whether rice husk derived biochar, a ubiquitous waste product in rice growing regions, can be beneficially recycled and surface modified to produce a low cost water treatment material.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au

Interactions of engineered nanoparticles with plant nutrients 

Centre/Institute: Future Industries Institute

Project keyword(s)*: agriculture, fertilizers, nanoparticles, plant nutrients, soils.

Recent studies have shown that application of engineered nanoparticles (ENPs) may potentially increase crop yield, but the exact mechanism for this is not known. One hypothesis is that ENPs influence nitrogen cycling in the soil resulting in increased nutrient availability to plants. In this project commercially available ENPs (e.g. nTiO2) will be initially tested for their association with nitrate in batch studies to ascertain the level of association. Subsequently, ENPs will be applied to a common agricultural soil dosed with increasing levels of nitrogen based fertilizers and the effect of the ENPs on nitrogen cycling observed via changes in microbial activity and nitrogen speciation.

Contact: Dr Elliot Duncan | T + 61 8 8302 5298 | E Elliott.Duncan@unisa.edu.au

Synthesis of amine functionalised carbon quantum dots for enhanced photosynthesis.

Centre/Institute: Future Industries Institute

Project keyword(s): agriculture, nanomaterials, photosynthesis, quantum dots,

Plants harvest sunlight via photosynthesis and convert it to electrochemical energy to drive important biochemical processes. However, surprising photosynthesis is a rather inefficient process, harvesting only a fraction of the available light energy and much effort has been directed to increasing photosynthetic efficiency using the luminescence effects of quantum dots. However, traditionally quantum dots have been composed of heavy metals making them unsuitable for agricultural applications because of their high biological toxicity. In this project, conducted with the International Rice Research Institute (Philippines), a range of nontoxic carbon based nanomaterials suitable for enhancing photosynthesis in rice and/or green algae will synthesized and their photosynthetic potential evaluated.

Contact: Dr Gary Owens | T + 61 8 8302 5043 | E Gary.owens@unisa.edu.au


 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Areas of study and research

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