Discovery Early Career Researcher Award - Grant ID: DE130100800
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Polymer micropatches applied to the skin for integrated capture and detection of circulating biomarkers. The purpose of this project is to develop a rapid and integrated technology for user-friendly biomarker detection at the point-of-care. We expect the device to rapidly detect proteins and/or antibodies, without the need for highly trained health workers or access to scientific laboratories.
Discovery Early Career Researcher Award - Grant ID: DE140101056
Funder
Australian Research Council
Funding Amount
$380,156.00
Summary
Rational Design of Plasmonic Nanoassemblies for Rapid and Multiplexed Point-of-Care Diagnosis by Surface-enhanced Raman Spectroscopy (SERS). The central aim of this project is to develop a novel technology/sensor platform for rapid, quantitative, multiplexed and highly sensitive point-of-care diagnostics using surface-enhanced Raman spectroscopy (SERS) as the read-out approach. Three-dimensional plasmonic superstructures as novel SERS labels will be synthesised and characterised at single-partic ....Rational Design of Plasmonic Nanoassemblies for Rapid and Multiplexed Point-of-Care Diagnosis by Surface-enhanced Raman Spectroscopy (SERS). The central aim of this project is to develop a novel technology/sensor platform for rapid, quantitative, multiplexed and highly sensitive point-of-care diagnostics using surface-enhanced Raman spectroscopy (SERS) as the read-out approach. Three-dimensional plasmonic superstructures as novel SERS labels will be synthesised and characterised at single-particle level and the choice of optimal SERS-active three-dimensional superstructures for use will be guided by empirical structure-activity correlations in combination with computer simulations. Tumour biomarkers for breast cancer will be employed as the model target for establishing the detection platform in a portable configuration for point-of-care diagnostics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170100068
Funder
Australian Research Council
Funding Amount
$390,000.00
Summary
Bioinspired liposome-based smart sensors. This project aims to develop a liposome-based biosensor technology that mimics cell sensory systems. Selective detection of compounds is increasingly important for food, health and environmental monitoring. Biosensor development faces long-standing challenges such as response time, sensitivity, specificity, and multiplexing. On the other hand, cells can sense and discriminate multiple biomolecules in seconds with high sensitivity and specificity. This pr ....Bioinspired liposome-based smart sensors. This project aims to develop a liposome-based biosensor technology that mimics cell sensory systems. Selective detection of compounds is increasingly important for food, health and environmental monitoring. Biosensor development faces long-standing challenges such as response time, sensitivity, specificity, and multiplexing. On the other hand, cells can sense and discriminate multiple biomolecules in seconds with high sensitivity and specificity. This project aims to harness cells’ exquisite biological properties to improve current detection techniques. It will integrate liposome-based sensors with microfluidics to perform analytical tasks ranging from food safety to diagnostics.Read moreRead less
Compression of distributed data: bridging the gap between theory and practice. In bushfire and tsunami early warning systems, environmental monitoring and healthcare applications, distributed sensors collect and transmit correlated data. This project will design novel data compression algorithms that exploit this correlation to dramatically increase the performance of existing networks and enable new applications.
Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the developmen ....Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the development of a cutting-edge technique to develop new intelligent applications in a decentralised and privacy-sensitive scenario. This game-changing research will advance current data mining and artificial intelligence research from centralised intelligence to decentralised intelligence with a collaboration network.Read moreRead less
Nanoarchitectured multifunctional porous superparamagnetic nanoparticles. This project aims to develop a method for the direct detection of biomarkers based on a new class of highly porous superparamagnetic nanoparticles with peroxidase-like activity. The particles will be used as dispersible capture agents for isolating specific targets in biological samples, and electrocatalytic nanozymes for naked-eye evaluation and electrochemical detection. The project is expected to develop simple, low-cos ....Nanoarchitectured multifunctional porous superparamagnetic nanoparticles. This project aims to develop a method for the direct detection of biomarkers based on a new class of highly porous superparamagnetic nanoparticles with peroxidase-like activity. The particles will be used as dispersible capture agents for isolating specific targets in biological samples, and electrocatalytic nanozymes for naked-eye evaluation and electrochemical detection. The project is expected to develop simple, low-cost, portable devices for the analysis of exosomes and exosomal miRNA in biological samples. The future development of this technology into diagnostic devices will improve patient outcomes by enabling earlier disease diagnosis and improved monitoring of treatment.Read moreRead less
Smart surfaces for monitoring cellular activity in real time: from multiple to single cells. Cells are the fundamental building block of life. In the proposed research smart surfaces will be developed that can monitor the release of enzymes from single cells and from multiple cells. This work will be important for developing cell chips for drug discovery, toxin detection and biomedical research and devices to monitor infection.
Cell Membrane Coated Photonic Crystal to study Receptor-Ligand Interactions. The current gold-standard assays for examining receptor-ligand interactions require expensive and costly fluorescent or radioactive labels or proteomics processes. This project aims to develop Artificial Photonic Cells by directly coating photonic crystals with cell membranes. The Artificial Photonic Cells retain the protein receptors in their native cell membrane environment and allow for label-free monitoring of the r ....Cell Membrane Coated Photonic Crystal to study Receptor-Ligand Interactions. The current gold-standard assays for examining receptor-ligand interactions require expensive and costly fluorescent or radioactive labels or proteomics processes. This project aims to develop Artificial Photonic Cells by directly coating photonic crystals with cell membranes. The Artificial Photonic Cells retain the protein receptors in their native cell membrane environment and allow for label-free monitoring of the receptor-ligand interactions using inexpensive miniature spectrometers - radically transforming these assays. This would generate fundamental and applied knowledge of materials sciences, photonic, and biointerfaces for label-free, ultra-sensitive, and selective assays to enable future drug and diagnostics target discovery. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100494
Funder
Australian Research Council
Funding Amount
$455,333.00
Summary
A novel electronic nose to locate victims of mass disaster events . The risk of global mass disaster events is increasing due to climate change and acts of terrorism. The most critical action following these events is locating victims. This proposal aims to develop an electronic nose capable of locating living and deceased victims by targeting volatile chemical components emitted from the human body. This project expects to overcome current limitations of current detection methods (e.g. cost, li ....A novel electronic nose to locate victims of mass disaster events . The risk of global mass disaster events is increasing due to climate change and acts of terrorism. The most critical action following these events is locating victims. This proposal aims to develop an electronic nose capable of locating living and deceased victims by targeting volatile chemical components emitted from the human body. This project expects to overcome current limitations of current detection methods (e.g. cost, limited operational time, deployment constraints in hazardous scenarios). The expected project outcomes include the development of innovative techniques that will improve mass disaster recovery on a global scale and provide significant benefit to human welfare. Read moreRead less
Secure Crowdsourcing Classification with Privacy Protection against Servers. This project aims to enable comprehensive quality data classification via secure crowdsourcing. The quality of a data-intensive process, such as a Machine Learning algorithm, depends on the input data quality. By using a crowdsourcing classification, the project expects to overcome the painstaking and costly process of humans correctly annotating extensive input data from diverse real information. The expected outcomes ....Secure Crowdsourcing Classification with Privacy Protection against Servers. This project aims to enable comprehensive quality data classification via secure crowdsourcing. The quality of a data-intensive process, such as a Machine Learning algorithm, depends on the input data quality. By using a crowdsourcing classification, the project expects to overcome the painstaking and costly process of humans correctly annotating extensive input data from diverse real information. The expected outcomes are innovative technologies, guaranteeing accuracy and confidentiality of annotation results whilst protecting the privacy of data classification results. It enhances data-intensive outputs quality, which will benefit large data-intensive applications, such as cybersecurity protections via intrusion detection.Read moreRead less