Description of field of research

Implantable medical devices such as brain machine interfaces (BMI), deep brain stimulators (DBS), vision prosthesis and cochlea implants use electric pulses to stimulate the central nervous system. These electronic devices have restored hearing to the deaf, functional sight to the profoundly blind, alleviate symptoms of drug resistant depression and epilepsy, and allowing those with locked-in syndrome the inability to move and speak due to brainstem injury or ALS to communicate and move again.

The parameter space for these stimulation pulse trains are enormous, involving for example, waveform shape, amplitude, duration, inter-pulse interval and phase. The pulses used in existing devices are based on educated guesstimation, and labour-intensive trial-and-error experiments. The combinatoric design space cannot be adequately nor efficiently addressed by such traditional, low-throughput technique. In this project you will help develop automated, high-throughput methods for finding effective stimulation waveforms.

  • Electronic implants for the peripheral nervous system (PNS) have successfully restored sensory and motor functions and are viable therapeutics for epilepsy and depression. But current devices have many shortcomings. We will develop mm-scale, wireless, and soft neurotechnology, capable of concurrent stimulation and recording in the PNS. Our devices will enable bidirectional neural interfacing with improved performance, stability, and reduced invasiveness.

    This project will help to develop mm-scale power harvesters of acoustic energy, delivered via ultrasound transducers. The energy collected will be used to power wireless, miniature neural implants placed deep inside the body. As an extension the acoustic interface could be augmented for communication, via backscatter echoes. We foresee this medical technology to drastically reduce invasiveness, by shrinking implant geometry from conventional cm-length-scale to mm-length-scale. Simultaneously it will enable efficient wireless power / data transfer over centimetres of body tissue, as opposed to the few-mm limit of traditional inductive power transfer methods, thereby enabling new classes of medical implants capable of operating deep within the body. 

    This project will involve laser-fabrication and benchtop testing of piezoelectric ceramics. Therefore, you should be very competent with analog circuit testing processes. We will train you on high-power laser fab.

    We are investigating the use of pluripotent stem cells to make blood vessels and blood. Our research has shown that the aorta and blood stem cells require a system that mimics the circulation of a human embryo. This project looks at design of a scalable microfluidic platform for manufacturing blood vessels and blood stem cells.

    This project will develop an automated microscale bioreactor to manufacture genetically modified cells for use in human cell and gene therapy. Existing large-footprint machines require skilled staff and complicated multi-step procedures, resulting in prohibitively expensive treatment costs, limiting accessibility, e.g. a cure for some cancers with CAR-T cells is possible but available to few, costing up to US$0.5 million. We have developed microfluidic technology for miniaturising and simplifying cell manufacture which reduces cost. 

    Nanoscience and nanotechnology arose in the last decades at the frontline of a broad range of research fields, due to the highly challenging and unexpected properties of nanomaterials and nanosystems. These properties enable nanomaterials in various shapes (0D, 1D, 2D, 3D) and phases or nanosystems to be applied in a wide range of applications, from biomedical to industrial engineering. Furthermore, these materials frequently serve as an interdisciplinary bridge between diverse scientific and technical fields.

    When dealing with the impacts of numerous processes occurring at the nanoscale, the application of machine learning (ML) techniques has become more attractive given the rising complexity of the data while enabling remarkable advancements that push forwards the broad spectrum of nanostructured materials-based technology. Furthermore, it is evident that, for such materials, theoretical formulations and ML-based solvers dealing with mesoscopic or macroscopic descriptions are required tools to improve experiments and practical investigations.

    The 3D genome organisation regulates gene expression by bringing distal regulatory elements, such as enhancers, to promoters in close spatial proximity. While many scientists have been working on cell-type specificity of gene regulation through transcriptomic sequencing, comprehensive investigation of cell-type specificity of 3D genome conformation patterns is still lacking. Recently, single-cell methods allow us to examine cell-type heterogeneity and profiling chromosome architecture at the single-cell level has been achieved using chromosome confirmation capture (Hi-C). However, unbiased and robust computational methods are urgently needed to study cell type-specific chromosome structural patterns and accurately identify local enhancer-promoter interactions at single-cell level.

    In recent decades a significant effort has been made toward the miniaturization of sensors to replace bulky, expensive, and complex analytical instruments used in the health and environmental sectors. Different techniques such as optical, piezoelectric and electrochemical methods have been used to make devices capable of detecting analytes of interest. Significantly, the electrochemical detection method has been one of the more common techniques used in diagnostics and environmental monitoring devices due to its accuracy, cost-effectiveness, and simplicity; however, the sensitivity and selectivity of the analytes of interest require further studies. 

    Nanotechnology, as a discipline to understand the matter at the nanoscale dimension involves the fabrication, manipulation, study of technique, material, modes and use of nano-devices in various applications. Nanomaterial-based sensors are highly sensitive and specific in their nature as compared to traditional material-based sensors.

    Genetic diseases and disorders (GDs) are usually caused by genomic variations in the DNA sequence ranging from 10% to 80%. There is, however, an important challenge in identifying responsible genomic variations. The genomic variants usually affect hundreds of genes; however, for only a subset of these variants the gene responsible for the phenotypes is known. Therefore, a computational framework is needed to provide a critical downstream analysis to prioritise long lists of genes. In this project, we will use Artificial Intelligence techniques to develop an innovative Deep Convolutional Neural Network Variants prioritisation model to detect the risk genetic variants in children with genetic diseases and disorders. We believe this innovative model will play a vital role in reducing barriers to clinical uptake of DNA testing and personalised medicine. The project will be carried using a national cohort of ~12,000 individuals, as well as ~45,000 publicly available individuals.

    A smart mouthguard is being developer for a novel sleep apnoea application. The mouth guard will collect patient signals (Spo2, audio, 9-axis interial) intra-orally for a whole night. Due to the very constrained size of the oral cavity, battery size must be minimised and thus low power firmware design/techniques are critical. 

    Biological signal/data collection is foundational to any research activity. We are developing a Bluetooth low energy based data-collection framework, that will work across different hardware sets and different micro-controller families (STM32, NRF52 etc).

    Biomechanics is a research field in which the human musculoskeletal system is studied using advanced engineering techniques, including computational modelling. One of the current challenges in this field is representing each individual, or patient, as accurately as possible to enable the development of personalised medicine and tailored treatments. This is a crucial requirement for improving orthopaedic practices and rehabilitation programmes, for example. The first step for studying the musculoskeletal system of an individual is the availability of an accurate representation of its anatomy at the skeletal level. 

    This project aims to develop a statistical model of the femur using a large dataset of lower limb bone surfaces obtained from medical images. A statistical shape model is a geometrical model that can describe the variability of a shape within a population. An example is provided in the attached figure, which represents a statistical shape model of the proximal tibia obtained from 35 bones. In this project, we are looking to develop a model of the femoral geometry from a larger training dataset and use it to reconstruct new bone anatomies using minimal geometrical information collected without imaging equipment.

    Polymer hydrogels are ecofriendly materials with a high-water content (generally >80%), in which hydrophilic polymer networks are highly solvated by water to produce a 3D tissue-like structure with outstanding flexibility. These soft materials have thus been widely used for flexible electronic devices, soft robotics, artificial skin and muscles, etc. However, their applications in artificial skin are greatly hampered in practice due to dissimilar surface structures of hydrogels with human skin. For instance, the loose networks with high volume of water in the hydrogel will cause inevitable water evaporation under ambient conditions which significantly reduces their flexibility and functionality.

    In this project, we will develop a simple yet robust synthetic strategy to chemically coat a thin layer of porous polymer on hydrogel surface that is similar as the human skin. The pores in the polymer film are exactly functioning as those in the human skin to breathe and modulate water evaporation of hydrogel by controlling their sizes. These polymer coatings will address the great challenge of uses of polymer hydrogels in multi-environment by mimicking human skin and will widen the applications of hydrogels in our real world.

    Modern society is developed based on a “carbon economy†where a great quantity of goods is built based on the carbon atom. Inevitably, the sustainable utilization of alternative carbon feedstocks, including captured CO2, natural gas (derived from biogas or abundant and less carbon-intensive natural gas),  and biomass, in an efficient and greener approach will become more and more important for the supply of C-embedded fuels and chemicals in our daily life with the gradual phasing out of the carbon-heavy fossil fuels (i.e., coal and heavy oil). This project aims to address this by resorting to the abundant solar energy and the knowledge of heterogeneous catalysis. The ideal catalyst (supported metal catalysts) will be synthesised and their activities towards important chemical reactions (for example, CO2 reduction and dry reforming of natural gas) under different conditions in home-made light-coupled reactor. Basic characterisations will be conducted to study the catalyst properties and reaction mechanism. The aim of the project is to provide important understandings in the selection of efficient catalysts which can harness the power of light for the production of demanding chemicals and fuels.

    Photoreforming is a process that utilises solar energy to activate photocatalysts for hydrogen production and at the same time organic oxidation [1]. Compared to overall water splitting, photoreforming offers a more energetically favourable pathway to directly transform renewable solar energy into hydrogen. In addition, photoreforming has the potential to simultaneously produce solar fuel and value-added chemicals. However, there have been only limited attempts to quantify the benefits of photoreforming through techno-economic feasibility analysis [2,3]. This research aims to estimate the levelized cost of hydrogen production via pilot-scale organic photoreforming under real sunlight irradiation. The findings will provide a significant basis to scale up renewable hydrogen production through photocatalysis.

    The material graphene (most widely explored carbon-based filler) was discovered in 2004 (Noble Prize awarded in 2010) it is the strongest material ever measured, and this is accompanied by a range of other extraordinary physical properties such as high thermal conductivity and high electrical conductivity. Graphene is seen as the material of the future, with the potential to revolutionize a wide range of industries from electronics to healthcare, and there is currently immense worldwide research activity in this area.  

    In this project, novel polymer/graphene-based nanocomposites with superior physical properties will be prepared. The addition of graphene as a component of polymer nanocomposites results in superior material properties it is a way of combining the best of both worlds (polymer and graphene). While the initial focus would be on graphene, project will also explore other filler materials including one dimensional carbon nanotubes (CNT). Both graphene and CNT are highly suitable materials for sound absorption application however, their direct application has major limitation including they cannot be directly applied on a substrate (require high temperature conditions known as chemical vapour deposition). To circumvent these limitations, this project will develop polymeric nanocomposite paints using graphene and CNT as fillers with ease of coating on any substrate. Using different emulsion-based polymerisation methods, nanocomposite paints with innate ability to undergo film formation at ambient temperature will be developed, which will subsequently be coated on substrates and subjected to acoustic absorption measurements. This project will combine experiments and computational modelling in close collaboration with Prof Nicole Kessissoglou (School of Mechanical and Manufacturing Engineering, UNSW) to develop next generation paints for sounds absorption application (for example in defence application).

    Hydrogen is a promising energy carrier for a sustainable future. Water electrolysis, if powered by renewable electricity, for instance, produced by photovoltaic cells and wind turbine, can produce hydrogen with almost zero carbon footprint. However, water electrolysis requires ultra-pure water as the feedstock for the production of hydrogen, which calls for high cost infrastructures and high energy inputs to purify the water resource (ocean, lake and river). In this project, we will develop robust water electrolysis devices that can produce high purity hydrogen from low quality and/or saline water. Devices developed in this project will further decrease the cost of hydrogen production and can be readily deployed in some costal areas and islands where freshwater is scarce. 

School

Chemical Engineering

Research Area

Electrocatalysis, nanomaterials, sustainable manufacturing, hydrogen economy

The project will be carried out in Particles and Catalysis Research Group. All facilities required for the completion of project are readily accessible. 

  • Novel electrodes for water electrolysis in low quality surface or saline water.
  • A practical system for hydrogen production using dirty water.
  • Publications and intellectual properties. 
  • Dr Xunyu Lu
  • Mr Yihao Shan
  • Modulating Pt-O-Pt atomic clusters with isolated cobalt atoms for enhanced hydrogen evolution catalysis, Nature Communications, 2022, 13, 2430.
  • Anchoring Sites Engineering in Single-Atom Catalysts for Highly Efficient Electrochemical Energy Conversion Reactions, Advanced Materials, 2021, 33, 2102801
  • A sea-change: manganese doped nickel/nickel oxide electrocatalysts for hydrogen generation from seawater, Energy & Environmental Science, 2018, 11, 1898-1910.