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Updated: Oct 25, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Inducing Tunable Volatile Analog Switching in Nanocomposite-Based MAPbBr3 Perovskite Memristors for Synaptic
Pablo F Betancur1, Miriam Minguez-Avellan1, Ismael Fernandez-Guillen2
1Instituto de Ciencia de los Materiales de la Universidad de Valencia (ICMUV), Paterna, València46980, Spain.
Abstract:
Metal halide perovskites (MHPs) are promising candidates for memristive applications owing to their mixed ionic-electronic conductivity, yet their inherent instability and tendency toward abrupt resistive switching remain critical barriers to neuromorphic implementations. Here, we present a nanocomposite strategy in which MAPbBr3 nanocrystals are synthesized in situ within a nickel acetate matrix through a one-step, annealing-free, and glovebox-free process, enabling ambient-stable memristive devices. By modulating the perovskite volume fraction, the ionic-electronic dynamics are tuned, inducing a transition from abrupt, nonvolatile digital switching in polycrystalline MAPbBr3 to gradual, volatile analog switching in the nanocomposite. Impedance spectroscopy and photophysical analyses attribute this transition to a shift from ion migration-driven switching in the bulk material to interfacial charge trapping and detrapping in the nanocomposite. The resulting volatile memristors emulate key synaptic functionalities, including short- and long-term plasticity, paired-pulse facilitation, and spike-dependent weight modulation, with stable performance over 1000 cycles under ambient conditions. Exploited in a reservoir computing framework, the nanocomposite memristor achieves ∼89% classification accuracy on noisy digit patterns with a linear readout, rising to 98% with a deeper network. This scalable, solution-processed approach offers a viable route to analog memristors for neuromorphic and edge computing.
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