Discovery Early Career Researcher Award - Grant ID: DE200101045
Funder
Australian Research Council
Funding Amount
$330,756.00
Summary
Enhanced methods for approximating the structure of large networks. This project aims to explain fundamental structural features of real-world networks such as the internet and online social networks, by advancing complex-analytical techniques. Current knowledge of properties such as reliability, robustness and optimal allocation of resources rely on assumptions that are invalid in real applications. The project expects to improve understanding of inhomogeneous network models by introducing an i ....Enhanced methods for approximating the structure of large networks. This project aims to explain fundamental structural features of real-world networks such as the internet and online social networks, by advancing complex-analytical techniques. Current knowledge of properties such as reliability, robustness and optimal allocation of resources rely on assumptions that are invalid in real applications. The project expects to improve understanding of inhomogeneous network models by introducing an innovative idea of high-order approximations to complex random settings. Expected outcomes include new tools for approximate counting of discrete objects satisfying given constraints. Applications of these tools could have far-reaching benefits to researchers who study quantitative characteristics of discrete systems.Read moreRead less
Deep ocean thermodynamics and climate change. This project aims to obtain new insights into the thermodynamic and transport properties of mixtures containing water, particularly at high pressures, that impact directly on our understanding of climate change processes. The project will involve the use of a polarisable potential for water which has recently been demonstrated to yield predictions of high accuracy. It will be used to model saline water mixtures containing carbon dioxide, resulting in ....Deep ocean thermodynamics and climate change. This project aims to obtain new insights into the thermodynamic and transport properties of mixtures containing water, particularly at high pressures, that impact directly on our understanding of climate change processes. The project will involve the use of a polarisable potential for water which has recently been demonstrated to yield predictions of high accuracy. It will be used to model saline water mixtures containing carbon dioxide, resulting in valuable data for thermodynamic properties of the world's oceans. These data are of crucial importance for accurate climate change predictions and as such the project will have an important impact on understanding our changing environment.Read moreRead less
Linking topology and rheology for designing supramolecular polymer networks. This project aims to develop a foundation for understanding how microscopic topology and intermolecular interactions control the flow behaviour of supramolecular polymer networks. Brownian dynamics algorithms will be developed to unravel the complex dynamics of the network and calibrated by comparison with carefully designed experiments. The expected outcome of the project is a quantitative framework for connecting the ....Linking topology and rheology for designing supramolecular polymer networks. This project aims to develop a foundation for understanding how microscopic topology and intermolecular interactions control the flow behaviour of supramolecular polymer networks. Brownian dynamics algorithms will be developed to unravel the complex dynamics of the network and calibrated by comparison with carefully designed experiments. The expected outcome of the project is a quantitative framework for connecting the molecular structure and energy landscape with resulting macroscopic properties. This project should yield significant benefit in the rational design of supramolecular systems in which the thermorheological properties can be tuned over a wide range of force/time scales with applications spanning from enhanced oil recovery to injectable hydrogels.Read moreRead less
Molecular design of complex lubricants to reduce friction. We will investigate the molecular level design of friction modifiers for a new generation of industrial lubricants. The goal is to dramatically reduce friction between moving mechanical parts, hence increasing energy efficiency in machines and reducing global greenhouse gas emissions. We will design and test these new friction modifiers by a combination of theoretical and computational methods based in statistical mechanics and nonequili ....Molecular design of complex lubricants to reduce friction. We will investigate the molecular level design of friction modifiers for a new generation of industrial lubricants. The goal is to dramatically reduce friction between moving mechanical parts, hence increasing energy efficiency in machines and reducing global greenhouse gas emissions. We will design and test these new friction modifiers by a combination of theoretical and computational methods based in statistical mechanics and nonequilibrium molecular dynamics and directly compare results with experimental measurements. Our investigations will pave the way to develop new cost-effective friction modifiers without the need for traditional and costly trial and error laboratory based experimentation.Read moreRead less