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Huang Lab

Bioengineering, Materials Engineering & Biointerface
 

We used a 3D printing technique, also known as additive manufacturing, to make composite electronic fibres, each 100 times thinner than a human hair, creating sensors beyond the capabilities of conventional film-based devices. Using this technique we made non-contact, wearable, portable respiratory sensors. These printed sensors are high-sensitivity, low-cost and can be attached to a mobile phone to collect breath pattern information, sound and images at the same time. The developed fibre sensors were used to test the amount of breath moisture leaked through face coverings, for respiratory conditions such as normal breathing, rapid breathing, and simulated coughing. The fibre sensors significantly outperformed comparable commercial sensors, especially in monitoring rapid breathing, which replicates shortness of breath.

In addition to the respiratory sensors, the printing technique can also be used to make biocompatible fibres of a similar dimension to biological cells, which enables them to guide cell movements and ‘feel’ this dynamic process as electrical signals. Also, the fibres are so tiny that they are invisible to the naked eye, so when they are used to connect small electronic elements in 3D, it would seem that the electronics are ‘floating’ in mid-air.

 

https://doi.org/10.1126/sciadv.aba0931

 


Using a new fibre production method called dynamic near-field electrospinning (dNFES), we have developed a bio-inspired acoustic sensor that ‘feels’ its environment in a similar way to how a spider feels acoustic vibrations in its web. The electronic device, which detects sound levels, makes use of a polymer nanofibre mesh to generate an electric charge when activated. The result is a self-powered sensor of high sensitivity and transparency, with a broad audible frequency bandwidth, making it potentially suitable for use in a number of applications, ranging from environmental detection through to biomedical diagnostics and the Internet of Things (IoT).

https://doi.org/10.1002/smll.202000581