We demonstrate a multipore nanofluidic memristor with conical pores showcasing a wide range of hysteresis and memristor properties that provide functionalities for brainlike computation in neuromorphic applications. Leveraging the interplay between the charged functional groups on the pore surfaces and the confined ionic solution, the memristor characteristics are modulated through the electrolyte type, ionic concentrations, and pH levels of the aqueous solution. The multipore membrane mimics the functional characteristics of biological ion channels and displays synaptical potentiation and depression. Furthermore, this property can be inverted in polarity by chemically varying the pH level. The ability to modulate memory effects by ionic conductivity holds promise for enhancing signal information processing capabilities.
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http://dx.doi.org/10.1021/acs.jpclett.3c02796 | DOI Listing |
J Phys Chem Lett
August 2024
Departament de Física de la Terra i Termodinàmica, Universitat de València, E-46100 Burjassot, Spain.
Multipore membranes with nanofluidic diodes show memristive and current rectifying effects that can be controlled by the nanostructure asymmetry and ionic solution characteristics in addition to the frequency and amplitude of the electrical driving signal. Here, we show that the electrical conduction phenomena, which are modulated by the interaction between the pore surface charges and the solution mobile ions, allow for a pH-dependent neuromorphic-like potentiation of the membrane conductance by voltage pulses. Also, we demonstrate that arrangements of memristors can be employed in the design of electrochemical circuits for implementing logic functions and information processing in iontronics.
View Article and Find Full Text PDFArtificial nanofluidic networks are emerging systems for blue energy conversion that leverages surface charge-derived permselectivity to induce voltage from diffusive ion transport under salinity difference. Here the pivotal significance of electrostatic inter-channel couplings in multi-nanopore membranes, which impose constraints on porosity and subsequently influence the generation of large osmotic power outputs, is illustrated. Constructive interference is observed between two 20 nm nanopores of 30 nm spacing that renders enhanced permselectivity to osmotic power output via the recovered electroneutrality.
View Article and Find Full Text PDFACS Nano
June 2024
The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 5267-0047, Japan.
Phys Rev E
April 2024
Departament de Física de la Terra i Termodinàmica, Universitat de València, E-46100 Burjassot, Spain.
We demonstrate that nanofluidic diodes in multipore membranes show a memristive behavior that can be controlled not only by the amplitude and frequency of the external signal but also by series and parallel arrangements of the membranes. Each memristor consists of a polymeric membrane with conical nanopores that allow current rectification due to the electrical interaction between the ionic solution and the pore surface charges. This surface charge-regulated ionic transport shows a rich nonlinear physics, including memory and inductive effects, which are characterized here by the current-voltage curves and electrical impedance spectroscopy.
View Article and Find Full Text PDFJ Am Chem Soc
May 2024
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
The biological neural network is a highly efficient in-memory computing system that integrates memory and logical computing functions within synapses. Moreover, reconfiguration by environmental chemical signals endows biological neural networks with dynamic multifunctions and enhanced efficiency. Nanofluidic memristors have emerged as promising candidates for mimicking synaptic functions, owing to their similarity to synapses in the underlying mechanisms of ion signaling in ion channels.
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