The next generation of waterproof smart fabrics will be laser printed and made in minutes.
This short article about an exciting and innovative new technology would be suited for use with students in year 4, 6, 8, 9, and 10 studying Physical or Chemical Sciences.
Word Count: 366
Australian scientists have developed a cost-efficient and scaleable method for rapidly fabricating textiles that are embedded with energy storage devices.
The technology enables graphene supercapacitors – powerful and long-lasting energy storage devices that are easily combined with solar or other sources of power – to be laser printed directly onto textiles.
Smart fabrics have diverse applications
Litty Thekkakara, a researcher in RMIT’s School of Science, says smart textiles with built-in sensing, wireless communication or health monitoring technology called for robust and reliable energy solutions.
“Current approaches to smart textile energy storage, like stitching batteries into garments or using e-fibres, can be cumbersome and heavy, and can also have capacity issues,” Thekkakara says.
“These electronic components can also suffer short-circuits and mechanical failure when they come into contact with sweat or with moisture from the environment.
New technology is fully washable
“Our graphene-based supercapacitor is not only fully washable, it can store the energy needed to power an intelligent garment – and it can be made in minutes at large scale.
“By solving the energy storage-related challenges of e-textiles, we hope to power the next generation of wearable technology and intelligent clothing.” The research analysed the performance of the proof-of-concept smart textile across a range of mechanical, temperature and washability tests and found it remained stable and efficient,” she says.
Min Gu from the University of Shanghai for Science and Technology, says the technology could enable real-time storage of renewable energies for e-textiles.
“It also opens the possibility for faster roll-to-roll fabrication, with the use of advanced laser printing based on multifocal fabrication and machine learning techniques.”
This article is republished from Australia’s Science Channel. Read the original article here.
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