Unravelling the dynamic transition between supercapacitive and memristive states in solid-state nanostructured ZnO-based neuromorphic device by impedance spectroscopy

The integration of energy storage and memory functionalities within a single platform is a critical step toward developing compact, multifunctional, and energy-efficient electronic systems. Conventional architectures implement supercapacitive and memristive functionalities using separate components, which compromises system efficiency, increases design complexity, and limits real-time adaptability in applications such as neuromorphic computing and renewable energy systems. In this work, we present a dual-functional coin-type symmetric device that unifies energy storage and neuromorphic memory capabilities using a nanostructured porous ZnO electrode and a chitosan/polyvinylidene fluoride (CS/PVDF) solid polymer blend electrolyte. The device demonstrates stable supercapacitive performance within a 4 V electrochemical window and exhibits clear memristive behavior at higher voltages under specific current compliances. The device transitions seamlessly between high and low resistance states under controlled pulse sequences, emulating various brain-inspired synaptic activities, such as short-term and long-term potentiation (STP and LTP) behaviors. Impedance spectroscopy, analyzed through the Havriliak–Negami model, reveals non-Debye relaxation dynamics and voltage-dependent ion-electron transport, offering valuable insights into the frequency-domain behavior underpinning the state transitions. This spectroscopic approach provides a deeper understanding of the dynamic switching mechanisms, highlighting the interplay between ionic mobility and electronic conduction across the device states. The unique neuromorphic characteristics combined with energy storage functionality in a single architecture mark a significant advancement in the physics and engineering of next-generation devices. This work lays the foundation for developing bio-inspired, low-power electronics that are both functionally versatile and physically compact, with promising implications for artificial intelligence hardware and integrated energy systems.