Discovery Early Career Researcher Award - Grant ID: DE210101549
Funder
Australian Research Council
Funding Amount
$395,775.00
Summary
A virtual platform for animal–human inhalation toxicity extrapolation. This project aims to remove the long-lasting barrier in extrapolating data from animals to humans by developing an integrated virtual platform. This project expects to fully resolve inhalation exposure differences in nasal airways between commonly used animal surrogates and humans, which could lay scientific underpinnings in developing rigorous interspecies data conversion schemes. Expected outcomes include a versatile inhala ....A virtual platform for animal–human inhalation toxicity extrapolation. This project aims to remove the long-lasting barrier in extrapolating data from animals to humans by developing an integrated virtual platform. This project expects to fully resolve inhalation exposure differences in nasal airways between commonly used animal surrogates and humans, which could lay scientific underpinnings in developing rigorous interspecies data conversion schemes. Expected outcomes include a versatile inhalation exposure risk assessment tool that can be implemented for any airway compartment, enhanced reliability of animal tests, reduced number of animals for testing. This should provide significant benefits in improving occupational health and safety and promoting National/International regulatory changes. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180101138
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
A multi-scale risk assessment platform for inhaled carbon nanotubes. This project aims to develop a coherent risk assessment platform to evaluate human respiratory exposure to carbon nanotubes. Compared to the exponential growth of carbon nanotubes technology, capability of inhalation risk assessment is lagging. The project expects to generate new knowledge on the unique role and risk of carbon nanotube geometry. It will develop a new transport model and create a unified risk assessment. The exp ....A multi-scale risk assessment platform for inhaled carbon nanotubes. This project aims to develop a coherent risk assessment platform to evaluate human respiratory exposure to carbon nanotubes. Compared to the exponential growth of carbon nanotubes technology, capability of inhalation risk assessment is lagging. The project expects to generate new knowledge on the unique role and risk of carbon nanotube geometry. It will develop a new transport model and create a unified risk assessment. The expected outcome is the enhanced risk assessment capability of human exposure to carbon nanotubes, which will provide a significant benefit to the nanotechnology industry through ensuring safety in developing an emergent technology.Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less