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Posted on November 29, 2019 by  & 

Wood is the Surprising Ingredient in Electrodes for Wearables

Wood fibres are being used by researchers at KTH to create a new class of stronger and lower-cost electrodes for even lighter and long-lasting flexible electronics and wearables. For more information see the IDTechEx report on Printed, Organic and Flexible Electronics 2020-2030: Forecasts, Technologies, Markets.
A team at KTH reports that it created the new composite material by combining wood cellulose nano fibrils (CNF) - or extremely small filaments known as nanorods - with MXene, a two-dimensional nanoscale conductive material. The wood fibrils provide mechanical strength otherwise lacking in MXenes, and they allow the electrodes to become flexible. For more information see the IDTechEx report on Wearable Technology Forecasts 2019-2029.
"Our results will eventually help with realizing the development of flexible multifunctional energy storage devices, that is, supercapacitors and batteries, at a lower cost and with higher device-base performance," says Max Hamedi, a researcher in wood cellulose at KTH who in recent years also developed a soft battery made of aerogel foam from wood pulp.
Hamedi says the electrodes can be used in any energy storage device but the most valuable application would be in flexible batteries and supercapacitors for wearable sensor devices.
"The electrode will provide both the strength and capacitive charge storage properties, which will enable them to last much longer in electrochemical devices," Hamedi says. "We hope these properties will help to make sustainable multifunctional batteries and supercapacitors."
Hamedi says the composite strength of the material is a result of an advantageous blend of geometry and chemistry. The cellulose nano fibrils bind to the MXene flakes, but they also interlock in the MXenes in their own random networks. "If we for example have the wrong geometrical match between the size of the flakes and the length of the CNF rods, then flakes would not be locked into the random network and we would have a much weaker composite."
Source and top image: KTH Royal Institute of Technology
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