Fractional-order memristive dynamics in colloidal graphitic carbon nitride systems.

Abstract

We report on the synthesis and characterization of a colloidal graphitic carbon nitride (g-C_{3}N_{4}) system exhibiting complex memfractance behavior. The g-C_{3}N_{4} colloid was prepared through thermal polymerization of urea, followed by dispersion in deionized water. X-ray diffraction and scanning electron microscopy confirmed the successful synthesis of g-C_{3}N_{4}. Electrical characterization revealed nonpinched hysteresis loops in current-voltage curves, indicative of memristive behavior with additional capacitive components. The device demonstrated stable resistive switching between high (∼50kΩ) and low (∼22kΩ) impedance states over 500 cycles, as well as synaptic plasticity-like conductance modulation. To capture these complex dynamics, we employed a generalized memfractance model that interpolates between memristive, memcapacitive, and second-order memristive elements. This model, employing fractional-order derivatives, accurately fitted the experimental data, revealing the device's memory effects. The emergence of memfractance in this colloidal system opens new avenues for neuromorphic computing and unconventional information processing architectures, leveraging the unique properties of liquid-state memory devices.