Using machine learning to improve efficiency of acoustic nanogenerators

Nanogenerators are an emerging class of technologies that produce electrical power from small amounts of mechanical or acoustic energy. These devices can be used to supply power to microelectronics or act as low-power sensors. Acoustic nanogenerators can harness energy from vibrations, monitor sound levels, or reduce noise.

Machine learning is another emerging field that has the potential to enhance many existing technologies. Yu et al. discussed employing machine learning and artificial intelligence (AI) to improve acoustic nanogenerator performance.

“AI and machine learning methods can perform data analysis and model training to improve the performance of nanogenerators and enable more efficient acoustic applications,” said author Kai Wang. “For example, AI algorithms can analyze real-time data from nanogenerators to optimize operating parameters such as frequency, amplitude, and resonance conditions. This ensures that the nanogenerator operates at its peak efficiency under varying environmental conditions.”

While machine learning algorithms have the potential to improve nanogenerator efficiency and enable new applications, introducing this technology into real-world environments will require additional efforts.

“Integrating nanogenerators with existing systems and ensuring compatibility across different materials and environments pose challenges,” said Wang. “Furthermore, addressing concerns related to the long-term reliability and durability of nanogenerator devices is paramount for sustained development.”

Still, the authors are hopeful that this technology will be adopted as part of a push for cleaner, more environmentally friendly technology.

“In tandem with the advancements in AI and machine learning, these nanogenerators are opening new frontiers in the field of acoustics by providing solutions that were once considered unattainable,” said Wang.

Source: “Application of nanogenerators in acoustics based on artificial intelligence and machine learning,” by Xiaofei Yu, Tengtian Ai, and Kai Wang, APL Materials (2024). The article can be accessed at https://doi.org/10.1063/5.0195399 .