Programming anisotropy into responsive soft materials. The project aims to generate viscoelastic soft materials with programmable anisotropy using aqueous suspensions of colloidal rods that have tunable surface coatings. The project expects to generate new knowledge in the rheology and structural characteristics of this unique class of materials. A key innovation is the use of charge-directed polymer self-assembly to control colloidal interactions, suspension rheology and phase behaviour. The in ....Programming anisotropy into responsive soft materials. The project aims to generate viscoelastic soft materials with programmable anisotropy using aqueous suspensions of colloidal rods that have tunable surface coatings. The project expects to generate new knowledge in the rheology and structural characteristics of this unique class of materials. A key innovation is the use of charge-directed polymer self-assembly to control colloidal interactions, suspension rheology and phase behaviour. The intended outcome is spatial control over the orientation of nanostructures, potentially mimicking the structural hierarchy found in nature. This should provide significant benefits to the creation of viscoelastic materials with complex rheology as well as structural, mechanical and optical heterogeneity.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC180100008
Funder
Australian Research Council
Funding Amount
$3,981,223.00
Summary
ARC Training Centre for Multiscale 3D Imaging, Modelling and Manufacturing. The ARC Training Centre for Multiscale 3D Imaging, Modelling and Manufacturing aims to connect the detailed microscopic characteristics of materials with their macroscopic properties and design characteristics of natural and manufactured structures. It will train a new generation of researchers and practitioners in the emerging discipline of Digital Materials. The approach allows optimisation at all scales, enabling cost ....ARC Training Centre for Multiscale 3D Imaging, Modelling and Manufacturing. The ARC Training Centre for Multiscale 3D Imaging, Modelling and Manufacturing aims to connect the detailed microscopic characteristics of materials with their macroscopic properties and design characteristics of natural and manufactured structures. It will train a new generation of researchers and practitioners in the emerging discipline of Digital Materials. The approach allows optimisation at all scales, enabling cost reductions and performance enhancements in key industries, including Oil, Gas and Energy Resources, Medical Technologies, and Advanced Manufacturing. The Centre expects to reduce the time needed in the prototyping cycle and product development, increasing industry’s capacity for accelerated innovation. The developments will build world-class Australian capabilities for developing high-value scaleable production of bespoke products and optimised process design.Read moreRead less
Transforming Australian bio-based industries through multiscale modelling. Agricultural and forestry biomass can be converted into feedstocks for production of biofuels and biomaterials via synthetic biology. A key challenge is the complex biomass microstructure renders it highly resistant to conversion, and pretreatment is crucial for enhancing process efficiency. Micro-CT imaging will enable particle characterisation and identification of changes in the fibre composition during pretreatment. T ....Transforming Australian bio-based industries through multiscale modelling. Agricultural and forestry biomass can be converted into feedstocks for production of biofuels and biomaterials via synthetic biology. A key challenge is the complex biomass microstructure renders it highly resistant to conversion, and pretreatment is crucial for enhancing process efficiency. Micro-CT imaging will enable particle characterisation and identification of changes in the fibre composition during pretreatment. This information will be used to create a virtual biomass particle model for an in silico investigation to inform optimal process design. The framework will transform the way biomass is processed, contributing to the growth of the Australian bio-manufacturing industry by making it more productive, profitable and sustainable.Read moreRead less
Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algori ....Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics. Read moreRead less
High performance bioderived hybrid fillers for rubber composite. This project aims to address a significant problem in polymer composite synthesis by production and application of high performance bioderived hybrid silica fillers from renewable biomass feedstock. The project expects to generate new knowledge in the area of advanced manufacturing using interdisciplinary approaches in biorefining, filler and composite production and characterization. Expected outcomes of this project include a mo ....High performance bioderived hybrid fillers for rubber composite. This project aims to address a significant problem in polymer composite synthesis by production and application of high performance bioderived hybrid silica fillers from renewable biomass feedstock. The project expects to generate new knowledge in the area of advanced manufacturing using interdisciplinary approaches in biorefining, filler and composite production and characterization. Expected outcomes of this project include a more sustainable filler production process for producing novel bioderived silica fillers with properties superior to commercial silica fillers. The successful implementation of this project will lead to the development of a new advanced manufacturing industry, creating jobs in regional Australia. Read moreRead less
Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the ext ....Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the extent of such biases, and develop models that are both more socially equitable, as well as less prone to expose private data in the learned representations. In doing so, it will make NLP more accessible to new populations of users, and remove socio-technological barriers to NLP uptake.Read moreRead less
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101297
Funder
Australian Research Council
Funding Amount
$429,000.00
Summary
A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the e ....A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the emerging field of biological imaging and to deliver an integrated imaging platform for mapping various tissue microscopic components at the cellular level. Successful outcomes have the potential for commercialisation and will accelerate a range of fundamental science and engineering studies requiring imaging techniques.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Leveraging 3D computer vision for camera-based precise geo-localisation. This project aims to develop advanced 3D computer vision and image processing technology that can turn regular cameras into high-precision location-sensing devices. Spatial Location is a fundamental type of information of our physical world. Determining the precise location of people, vehicle, and mobile devices is essential for many critical applications. Outcomes of the project will enable a wide range of novel applicatio ....Leveraging 3D computer vision for camera-based precise geo-localisation. This project aims to develop advanced 3D computer vision and image processing technology that can turn regular cameras into high-precision location-sensing devices. Spatial Location is a fundamental type of information of our physical world. Determining the precise location of people, vehicle, and mobile devices is essential for many critical applications. Outcomes of the project will enable a wide range of novel applications of significant social, environmental and economic value, such as Location-Aware Service, Environment Monitoring, Augmented Reality, Autonomous Vehicle, and Rapid Emergency Response. The project will enhance Australia's international competitive advantage in forefront of ICT research and technology innovation.Read moreRead less