3D-Printed "Invisible Fibers" Make for Low-Cost, High-Sensitivity Sensors — and Prove Masks Work
Lightweight, cheap, and easy-to-use, the fiber sensors are being investigated for healthcare and bio-computer interface uses.
Researchers at the University of Cambridge have developed a technique for 3D printing "invisible fibers," 100 times thinner than a human hair, for next-generation wearable sensors — and have proven the concept by measuring moisture leakage through face masks.
"Sensors made from small conducting fibers are especially useful for volumetric sensing of fluid and gas in 3D, compared to conventional thin-film techniques," explains research lead Dr. Yan Yan Shery Huang, "but so far, it has been challenging to print and incorporate them into devices, and to manufacture them at scale."
"Our fiber sensors are lightweight, cheap, small and easy to use, so they could potentially be turned into home-test devices to allow the general public to perform self-administered tests to get information about their environments."
The 3D-printed sensors are high sensitivity, low-cost, and when connected to a smartphone for data capture allow researchers to track multiple factors at once: breath pattern information, sound, and images. This was used in the prototype mask testing system, which tracked the moisture leaked through a face mask during normal breathing, rapid breathing, and simulated coughing — outperforming, the researchers claim, commercial sensors by a significant margin.
Data from the prototype was used to analyse various mask types, finding that fabric and surgical masks leak moisture — particles of which could carry viruses like SARS-CoV-19 — primarily through the front, while N95-grade masks leak most from the top and sides even when properly fitted. All mask types, though, were found to significantly weaken the flow of exhaled breath — which, in turn, makes it harder for the wearer to spread a virus.
The team is currently looking into using the same fiber-printing technique for other sensor types for mobile health monitoring and bio-machine interface applications. The paper, meanwhile, has been published in the journal Science Advances under open-access terms.