Maximizing Triboelectric Nanogenerators by Physics-Informed AI Inverse Design

Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought-after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real-life applications. Artificial intelligence-enabled inverse design is a powerful tool to realize performance-oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics-informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four-mode analytical models by physics-informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence-enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics-informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real-life applications is discussed. Developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs. This paper provides insights into the current challenges of designing and optimizing triboelectric nanogenerators and presents physics-informed artificial intelligence inverse design strategies to tackle these challenges. The potential of physics-informed inverse design to propel triboelectric nanogenerators to intelligent systems is discussed.image

Automated summary

Triboelectric nanogenerators are a promising approach to harvesting energy from mechanical excitations. However, developing nanogenerators with specific output remains a challenge due to the uncertainties associated with their complex designs. This paper reviews the analysis of triboelectricity, discusses the current challenges of designing and optimizing nanogenerators, and highlights the potential of physics-informed artificial intelligence inverse design strategies to tackle these challenges. The paper also explores the potential of physics-informed inverse design to propel nanogenerators to intelligent systems for real-life applications.