We have shown that glassy carbon is a fascinating material which has different properties depending on thow it was formed. The effect on how order and impurities influences the new phases formed under pressure is not understood.

Fusion energy promises millions of years of clean energy, but puts extreme stress on materials. This research will resolve scientific issues surrounding plasma-material interactions to guide and facilitate development of future advanced materials for fusion reactors.

This is a multidisciplinary project supported by the ANU Grand Challenge project ‘Our Health in Our Hands’ (OHIOH), aimed at developing wearable sensors for detecting target biomarkers to identify certain health conditions.

Controlling the flow of ions and molecules through nano-sized pores is fundamental in many biological processes and the basis for applications such as DNA detection, water desalination and drug delivery. The project aims to develop solid-state nanofluidic diodes and exploit their properties for applications in bio-sensors and ion-selective channels.

Nano-pore membranes have important applications in chemical- and bio-sensing, water filtration and protein separation. This project will investigate our innovative technology to fabricate nanopore membranes in silicon dioxide and silicon nitride and exploit their use for advanced applications.

Absolute gravimeters tie their measurement of gravity to the definition of the second 
by interrogating the position of a falling test mass using a laser interferometer. Our vision is to develop and prototype a miniaturised absolute gravimeter by 
leveraging modern vacuum, laser, and micro-electromechanical systems.

New forms of materials can be made using extreme pressures via diamond anvil cells.

The hexagonal form of sp3 bonded carbon is predicted to be harder than 'normal' cubic diamond. We can make tiny amounts of this new form of diamond and want to know if it really is harder than diamond.

Investigate novel nanopore bio-sensors using nanofabrication, bio-chemsity and machine learning.