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Current Selection
Status : Active
Research Topic : functional analysis
Socio-Economic Objective : Water Safety
Australian State/Territory : SA
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  • Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE210100253

    Funder
    Australian Research Council
    Funding Amount
    $450,948.00
    Summary
    Functional carbon hybrids for green catalysis and clean water. This project aims to develop a family of structure-tailored, robust and metal-free carbon hybrids and environmental-benign processes for catalytic degradation of emerging microcontaminants in water. Innovations are expected in the design of reaction-oriented nanocarbons, new concept in atomic level carbocatalysis from computation and in-situ characterisation, advanced purification technology, and breakthroughs in material engineering .... Functional carbon hybrids for green catalysis and clean water. This project aims to develop a family of structure-tailored, robust and metal-free carbon hybrids and environmental-benign processes for catalytic degradation of emerging microcontaminants in water. Innovations are expected in the design of reaction-oriented nanocarbons, new concept in atomic level carbocatalysis from computation and in-situ characterisation, advanced purification technology, and breakthroughs in material engineering. The anticipated outcomes will be the scientific basis for functional nanomaterials, nanotechnology, and green remediation technologies. Success will provide significant benefits in securing a sustainable future for Australia, with clean water and strategies for advanced manufacturing in related areas.
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    Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE220101074

    Funder
    Australian Research Council
    Funding Amount
    $424,500.00
    Summary
    Conversion of biowastes to porous carbon materials for green catalysis. This project aims to develop a family of biowaste-derived porous carbon and single-atom-anchored porous carbon catalysts for the degradation of emerging microcontaminants in water. Innovations are expected in systematically developing affordable, facile, productive, and sustainable approaches. Via reaction-oriented structure design, new concept will be defined at the atomic level using calculations and in situ characterisati .... Conversion of biowastes to porous carbon materials for green catalysis. This project aims to develop a family of biowaste-derived porous carbon and single-atom-anchored porous carbon catalysts for the degradation of emerging microcontaminants in water. Innovations are expected in systematically developing affordable, facile, productive, and sustainable approaches. Via reaction-oriented structure design, new concept will be defined at the atomic level using calculations and in situ characterisations in material engineering and advanced purification technology. The anticipated outcomes will provide fundamental knowledge in green nanotechnologies for water remediation. Success will secure a sustainable future for Australia with clean water and strategies for advanced manufacturing in relevant areas.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP200301507

    Funder
    Australian Research Council
    Funding Amount
    $396,000.00
    Summary
    Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed .... Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed methodology enables quantification of confidence in the predictions. This will provide ship owners, directly to their vessels and/or at the fleet management centres, information such as weather reports, reliable collision/no-collision warnings and avoidance strategies, on-the-fly.
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