Professor Huang leads the Biointerface Research Group at the Department of Engineering. She has been honoured by the BioMedEng Association for her “creative and pioneering” work, which advances sustainable and ethical biomedical technologies.
The Prize citation reads: “Professor Huang's work shows she has consistent dedication to rigorous research, pushing the boundaries of knowledge across multiple disciplines. Her group has translated scientific findings into exploring a new generation of tissue engineering constructs for potential personalised therapy, at affordable costs; and to provide new solutions for disease monitoring, drug testing, and better patient healthcare.”
On receiving the Prize, Professor Huang said: “I am profoundly honoured to receive this award and feel privileged to be recognised for my contributions. Receiving the award from Professor Karin Hing, a pioneer whose bone graft substitute technology has significantly enhanced the lives of countless patients, is particularly humbling. This recognition further fuels my commitment to advancing my technology for the broader health benefits of society, balancing sustainability and ethical practices.”
Professor Huang’s noteworthy contributions include publications in the journals Nature Electronics (2024), Nature Materials (2023) and Nature Communications (2021). They are listed as follows:
In this research, a method was developed to make adaptive and eco-friendly sensors that can be directly and imperceptibly printed onto a wide range of biological surfaces, including, but not limited to a human finger and a flower petal.
The method takes its inspiration from spider silk, which can conform and stick to a range of surfaces. These ‘spider silks’ also incorporate bioelectronics, so that different sensing capabilities can be added to the ‘web’.
The fibres, at least 50 times smaller than a human hair, are so lightweight that they can be printed directly onto the fluffy seedhead of a dandelion, for example, without collapsing its structure. When printed on human skin, the fibre sensors conform to the skin and expose the sweat pores, so the wearer does not detect their presence. Tests of the fibres printed onto a human finger suggest they could be used as continuous health monitors.
This low-waste and low-emission method for augmenting living structures could be used in a range of fields, from healthcare and virtual reality to electronic textiles and environmental monitoring. The results are reported in the journal Nature Electronics.
In this perspective, Professor Huang and her colleagues warn that the e-textile supply chain and its potential for scalable commercialisation could be “further complicated” by the associated global environmental burden, and the growing use of nanomaterials in e-textiles.
They say that some of these nanomaterials can pose environmental challenges and could also have adverse effects on human health (e.g. skin irritation and/or absorption of loose nanoparticles into the skin).
The research team propose the 4R e-textile design concept (repair; recycle; replace; reduce) alongside innovations in materials selection and biofabrication-inspired processing – a revolutionary approach that uses additive manufacturing processes to produce biomaterials, devices, cells and tissues.
The aim is to reach sustainable growth and balance economic returns/scalable commercialisation with “environmental consciousness”, at a time when consumers are actively aligning their purchasing behaviours with sustainability goals. The perspective can be accessed via the journal Nature Materials.
In this research, 3D printing has been used to create intricate replicas of human cochleae – the spiral-shaped hollow bone of the auditory inner ear – and combine them with machine learning. This is done to advance clinical predictions of ‘current spread’ inside the ear for cochlear implant (CI) patients.
‘Current spread’ or electrical stimulus spread, as it is also known, affects CI performance and leads to 'blurred' hearing for users, but no adequate testing models have previously existed for replicating the problem in human cochleae.
Thanks to 3D-printed cochleae with tuneable electro-anatomy, it has been made possible to analyse the electric field imaging data of cochlear implant patients.
The 3D-printed models allow the researchers to study how the cochlear shape and electrical property of the biomimetic cochleae affects ‘current spread’ measured in electric field imaging.
Once combined with machine learning, this co-modelling method can predict the ‘current spread’ in cochlear implant users and, for the first time, extrapolate the range of patient cochlear tissue resistivity. Thus, 3D printing-machine learning co-modelling could be an ethical and privacy-respected alternative to the existing populational data collection for training machine learning algorithms. The results are reported in the journal Nature Communications.
