Dual-ion electrochemical systems. The project aims to develop dual-ion electrochemical systems. In contrast to conventional single-ion rechargeable cells, the charge storage process in the cathodes of these devices is facilitated by a second, negative ion. Dual-ion systems represent robust alternatives to current lithium-ion batteries and lithium-ion capacitors, addressing their sustainability and energy density limitations. The project’s outcomes are in the form of new sustainable energy storag ....Dual-ion electrochemical systems. The project aims to develop dual-ion electrochemical systems. In contrast to conventional single-ion rechargeable cells, the charge storage process in the cathodes of these devices is facilitated by a second, negative ion. Dual-ion systems represent robust alternatives to current lithium-ion batteries and lithium-ion capacitors, addressing their sustainability and energy density limitations. The project’s outcomes are in the form of new sustainable energy storage technologies with attrative energy and power densities for a wide range of applications. This should provide a significant benefit to society, the economy and the environment in enabling an easier transition to clean energy and ensuring energy security in Australia.Read moreRead less
X-ray imaging and magnetic resonance approach for enhanced oil recovery. This project aims to develop an efficient multi-scale modelling capability to quantify the effect of two-phase fluid flow within porous material by modelling rock wettability heterogeneity and alteration on two-phase flow performance for heterogeneous rock. Super-resolution methods combined with a deep learning approach will be used to determine a digital representation of reservoir rock, achieving an unprecedented combinat ....X-ray imaging and magnetic resonance approach for enhanced oil recovery. This project aims to develop an efficient multi-scale modelling capability to quantify the effect of two-phase fluid flow within porous material by modelling rock wettability heterogeneity and alteration on two-phase flow performance for heterogeneous rock. Super-resolution methods combined with a deep learning approach will be used to determine a digital representation of reservoir rock, achieving an unprecedented combination of resolution necessary to resolve small-scale fluid connectivity and field of view required to capture heterogeneity. The project expects to develop a workflow to populate a high-resolution model with wettability parameters by combining micro-CT imaging with nuclear magnetic resonance measurements. This improved understanding should provide significant benefits by enhancing our capability to optimise enhanced oil and gas recovery programs.Read moreRead less
Parasite virulence: the role of activation and suppression of P2X7 receptors. Toxoplasmosis and leishmaniasis pose a threat to the health and well-being of the Australian human and animal populations. Around 6-8 million Australians are infected with Toxoplasma, a parasite that can cause severe problems in immunosuppressed individuals and birth defects and miscarriage in pregnant women with a primary infection. Toxoplasmosis is also the main cause of abortion and stillbirth in Australian sheep. L ....Parasite virulence: the role of activation and suppression of P2X7 receptors. Toxoplasmosis and leishmaniasis pose a threat to the health and well-being of the Australian human and animal populations. Around 6-8 million Australians are infected with Toxoplasma, a parasite that can cause severe problems in immunosuppressed individuals and birth defects and miscarriage in pregnant women with a primary infection. Toxoplasmosis is also the main cause of abortion and stillbirth in Australian sheep. Leishmaniasis, recently found in Australia, is a risk for overseas travellers, livestock and wildlife. This research will provide an understanding of what makes these parasites successful, paving the way for development of novel drugs to combat these chronic diseases.Read moreRead less
Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored ....Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.Read moreRead less