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AI Enhances Nanofiber Acoustic Energy Harvesters
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AI Enhances Nanofiber Acoustic Energy Harvesters
by Clarence Oxford
Los Angeles CA (SPX) Jun 05, 2024

Scientists at the Terasaki Institute for Biomedical Innovation (TIBI) have utilized artificial intelligence to improve the design and production of nanofibers used in wearable nanofiber acoustic energy harvesters (NAEH). These devices capture environmental sound energy and convert it into electrical energy for applications like hearing aids.

Efforts to harness natural energy sources have led to advancements like solar panels and wind turbines. Similarly, acoustic energy conversion is used in devices such as microphones and flexible electronics for personalized healthcare.

Piezoelectric nanogenerators, which convert mechanical vibrations into electrical power, have garnered interest as acoustic energy harvesters. However, these nanogenerators mainly convert high-frequency sound waves, whereas most environmental sounds are low-frequency. Additionally, optimizing materials, design, and fabrication parameters poses challenges.

According to their paper in Nano Research, TIBI scientists tackled these issues by selecting polyvinylfluoride (PVDF) for nanofiber fabrication, known for its acoustic energy capture efficiency. Polyurethane (PU) was added to the PVDF solution for flexibility, and electrospinning was used to produce the composite PVDF/PU nanofibers.

The team then applied AI techniques to optimize fabrication parameters in electrospinning PVDF/PU nanofibers, including applied voltage, spinning time, and drum rotation speed. This approach maximized power generation from the nanofibers.

For their nanoacoustic energy harvester, TIBI scientists created a nanofibrous mat from PVDF/PU nanofibers, sandwiched between aluminum mesh layers serving as electrodes, and encased the assembly in flexible frames.

Tests showed that the AI-optimized PVDF/PU NAEHs performed significantly better than conventionally fabricated ones, with over 2.5 times higher power density and greater energy conversion efficiency (66% vs 42%). The AI-generated NAEHs were effective across a wide range of low-frequency sounds, enhancing sound recognition and word distinction.

"Models using artificial intelligence optimization, such as the one described here, minimize time spent on trial and error and maximize the effectiveness of the finished product," said Ali Khademhosseini, Ph.D., TIBI's director and CEO. "This can have far-reaching effects on the fabrication of medical devices with significant practicability."

Research Report:A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

Related Links
Terasaki Institute for Biomedical Innovation
Powering The World in the 21st Century at Energy-Daily.com

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