A micro resonating motor based on neuron action potential

This paper introduces a novel bioelectromechanical device converting the electrochemical potential energy of excitable cells into mechanical work by coupling the Hodgkin–Huxley (HH) neuronal model response to a mechanical resonator. Addressing key challenges in bioelectromechanical systems, including biocompatibility, miniaturization, and efficient energy conversion, the device leverages the membrane potentials of biological cells to drive mechanical oscillations within microelectromechanical systems (MEMS). Through a combination of numerical simulations and theoretical analyses, it is demonstrated that the coupled HH–resonator system achieves stable limit cycles and significant mechanical displacements via parametric amplification. This amplification arises from the nonlinear capacitive coupling, which leads to Mathieu-like equations governing the system’s dynamics, thereby enabling large oscillations from relatively small voltage inputs. Such parametric resonance is critical for the device's ability to sustain oscillatory motion, making it highly suitable for integration into compact and implantable MEMS applications. Potential applications include implantable sensors and actuators for real-time physiological monitoring, and advanced micro-scale systems that benefit from biologically sourced energy. The findings underscore the promise of bioelectromechanical systems in advancing biomedical and microengineering technologies, paving the way for innovative solutions in personalized medicine, bio-robotics, and beyond. • A novel bioelectromechanical device converting neuronal action potentials into mechanical energy is proposed. • The Hodgkin–Huxley model is coupled to a mechanical resonator, demonstrating stable oscillations through parametric amplification. • The device efficiently converts electrochemical potential from excitable cells into mechanical work, suitable for MEMS integration. • Potential biomedical applications include implantable sensors, neuroprosthetics, and real-time physiological monitoring. • The proposed approach advances the field of bioengineering by combining neural signals with mechanical actuation.