Self-Powered, Neural Network-Inspired Mechanical Intelligence Driven by Triboelectric Nanogenerators

Autonomous computing systems are crucial for distributed intelligent electronics, yet conventional architectures continue to face challenges in energy efficiency and system integration. Here, we report a self-powered mechanical intelligence paradigm driven by triboelectric nanogenerators (TENGs). In this paradigm, mechanical logic units respond to charge inputs of different polarities to establish physical connections with a neural-network-like topology, enabling the construction of 14 basic logic gates. Information encoding and processing are thereby integrated into the mechanical response and structural evolution of the system. This approach, in which ordered logic functions emerge through physical interactions, provides a new pathway toward mechanical intelligence. The resulting self-powered mechanical intelligence integrates logic operations and numerical computation on a single platform. Furthermore, we demonstrate a self-sustained intelligent in-vehicle system, in which the TENG simultaneously provides sensing inputs and drives computation, enabling self-powered real-time monitoring of passenger status. These results demonstrate that TENG-driven self-powered mechanical intelligence can perform information processing, providing a practical pathway toward autonomous and low-maintenance intelligent systems based on mechanical intelligence.