Learning from the Brain: Bioinspired Nanofluidics

Unlocking Nanoprecipitation: A Pathway to High Reversibility in Nanofluidic Memristors

Solid-state nanochannels have emerged as a promising platform for the development of ionic circuit components with analog properties to their traditional electronic counterparts. In the last years, nanofluidic devices with memristive properties have attracted special interest due to their applicability in, for example, the construction of brain-like computing systems. In this work, an asymmetric track-etched nanofluidic channel with memory-enhanced ion transport is reported. The results illustrate that the formation of nanoprecipitates on the channel walls induces memory effects in ion transport, leading to characteristic hysteresis loops in the current-voltage curves, a hallmark of memristive behavior. Notably, these memristive properties are achievable with a straightforward experimental setup that combines an aqueous solvent and a relatively low-soluble inorganic salt. The various conductance states can be rapidly and reversibly tuned over prolonged time scales. Furthermore, under appropriate measurement conditions, the nanofluidic device can alternate between different iontronic regimes and states, encompassing ion current rectification, ON-OFF states, and memristor-like behavior. These findings provide insights into the design and optimization of nanofluidic devices for bioinspired ionic circuit components.

Ionic Potential Relaxation Effect in a Hydrogel Enabling Synapse-Like Information Processing

The next-generation brain-like intelligence based on neuromorphic architectures emphasizes learning the ionic language of the brain, aiming for efficient brain-like computation and seamless human-computer interaction. Ionic neuromorphic devices, with ions serving as information carriers, provide possibilities to achieve this goal. Soft and biocompatible ionic conductive hydrogels are an ideal substrate for constructing ionic neuromorphic devices, but it remains a challenge to modulate the ion transport behavior in hydrogels to mimic neuroelectric signals. Here, we describe an ionic potential relaxation effect in a hydrogel device prepared by sandwiching a layer of polycationic hydrogel (CH) between two layers of neutral hydrogel (NH), allowing this device to simulate various electrical signal patterns observed in biological synapses, including short- and long-term plasticity patterns. Theoretical and experimental results show that the selective permeation and hysteretic diffusion of ions caused by the anion selectivity of the CH layer are responsible for potential relaxation. Such an effect allows us with hydrogels to enable synapse-like information processing functions, including tactile perception, learning, memory, and neuromorphic computing. Additionally, the hydrogel device can operate stably even under 180° bending and 50% tensile strain, expanding the pathway for implementing advanced brain-like intelligent systems.

Neural Functions Enabled by a Polarity-Switchable Nanofluidic Memristor

Reproducing neural functions with artificial nanofluidic systems has long been an aspirational goal for neuromorphic computing. In this study, neural functions, such as neural activation and synaptic plasticity, are successfully accomplished with a polarity-switchable nanofluidic memristor (PSNM), which is based on the anodized aluminum oxide (AAO) nanochannel array. The PSNM has unipolar memristive behavior at high electrolyte concentrations and bipolar memristive behavior at low electrolyte concentrations, which can emulate neural activation and synaptic plasticity, respectively. The mechanisms for the unipolar and bipolar memristive behaviors are related to the polyelectrolytic Wien (PEW) effect and ion accumulation/depletion effect, respectively. These findings are beneficial to the advancement of neuromorphic computing on nanofluidic platforms.

Scan-Rate-Dependent Ion Current Rectification in Bipolar Interfacial Nanopores

This study presents a theoretical investigation into the voltammetric behavior of bipolar interfacial nanopores due to the effect of potential scan rate (1-1000 V/s). Finite element method (FEM) is utilized to explore the current-voltage (I-V) properties of bipolar interfacial nanopores at different bulk salt concentrations. The results demonstrate a strong impact of the scan rate on the I-V response of bipolar interfacial nanopores, particularly at relatively low concentrations. Hysteresis loops are observed in bipolar interfacial nanopores under specific scan rates and potential ranges and divided by a cross-point potential that remains unaffected by the scan rate employed. This indicates that the current in bipolar interfacial nanopores is not just reliant on the bias potential that is imposed but also on the previous conditions within the nanopore, exhibiting history-dependent or memory effects. This scan-rate-dependent current-voltage response is found to be significantly influenced by the length of the nanopore (membrane thickness). Thicker membranes exhibit a more pronounced scan-rate-dependent phenomenon, as the mass transfer of ionic species is slower relative to the potential scan rate. Additionally, unlike conventional bipolar nanopores, the ion current passing through bipolar interfacial nanopores is minimally affected by the membrane thickness, making it easier to detect.

Synaptic Response of Fluidic Nanopores: The Connection of Potentiation with Hysteresis

Iontronic fluidic ionic/electronic components are emerging as promising elements for artificial brain-like computation systems. Nanopore ionic rectifiers can be operated as a synapse element, exhibiting conductance modulation in response to a train of voltage impulses, thus producing programmable resistive states. We propose a model that replicates hysteresis, rectification, and time domain response properties, based on conductance modulation between two conducting modes and a relaxation time of the state variable. We show that the kinetic effects observed in hysteresis loops govern the potentiation phenomena related to conductivity modulation. To illustrate the efficacy of the model, we apply it to replicate rectification, hysteresis and conductance modulation of two different experimental systems: a polymer membrane with conical pores, and a blind-hole nanoporous anodic alumina membrane with a barrier oxide layer. We show that the time transient analysis of the model develops the observed potentiation and depression phenomena of the synaptic properties.

pH-Dependent Effects in Nanofluidic Memristors

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.

Nanofluidic Ionic Memristors

Living organisms use ions and small molecules as information carriers to communicate with the external environment at ultralow power consumption. Inspired by biological systems, artificial ion-based devices have emerged in recent years to try to realize efficient information-processing paradigms. Nanofluidic ionic memristors, memory resistors based on confined fluidic systems whose internal ionic conductance states depend on the historical voltage, have attracted broad attention and are used as neuromorphic devices for computing. Despite their high exposure, nanofluidic ionic memristors are still in the initial stage. Therefore, systematic guidance for developing and reasonably designing ionic memristors is necessary. This review systematically summarizes the history, mechanisms, and potential applications of nanofluidic ionic memristors. The essential challenges in the field and the outlook for the future potential applications of nanofluidic ionic memristors are also discussed.

Single-Pore Nanofluidic Logic Memristor with Reconfigurable Synaptic Functions and Designable Combinations

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. However, realizing chemical signal-modulated logic functions in nanofluidic memristors, which is the basis for brain-like computing applications, remains unachieved. Here, we report a single-pore nanofluidic logic memristor with reconfigurable logic functions. Based on the different degrees of protonation and deprotonation of functional groups on the inner surface of the single pore, the modulation of the memristors and the reconfiguration of logic functions are realized. More noteworthy, this single-pore nanofluidic memristor can not only avoid the average effects in multipore but also act as a fundamental component in constructing complex neural networks through series and parallel circuits, which lays the groundwork for future artificial nanofluidic neural networks. The implementation of dynamic synaptic functions, modulation of logic gates by chemical signals, and diverse combinations in single-pore nanofluidic memristors opens up new possibilities for their applications in brain-inspired computing.

Artificial Funnel Nanochannel Device Emulates Synaptic Behavior

Creating artificial synapses that can interact with biological neural systems is critical for developing advanced intelligent systems. However, there are still many difficulties, including device morphology and fluid selection. Based on Micro-Electro-Mechanical System technologies, we utilized two immiscible electrolytes to form a liquid/liquid interface at the tip of a funnel nanochannel, effectively enabling a wafer-level fabrication, interactions between multiple information carriers, and electron-to-chemical signal transitions. The distinctive ionic transport properties successfully achieved a hysteresis in ionic transport, resulting in adjustable multistage conductance gradient and synaptic functions. Notably, the device is similar to biological systems in terms of structure and signal carriers, especially for the low operating voltage (200 mV), which matches the biological neural potential (∼110 mV). This work lays the foundation for realizing the function of iontronics neuromorphic computing at ultralow operating voltages and in-memory computing, which can break the limits of information barriers for brain-machine interfaces.

Memristive arrangements of nanofluidic pores

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. Also, neuromorphiclike potentiation of the membrane conductance following voltage pulses (spikes) is observed. The multipore membrane with nanofluidic diodes shows physical concepts that should have application for information processing and signal conversion in iontronics hybrid devices.

Nanofluidic memristor based on the elastic deformation of nanopores with nanoparticle adsorption

The memristor is the building block of neuromorphic computing. We report a new type of nanofluidic memristor based on the principle of elastic strain on polymer nanopores. With nanoparticles absorbed at the wall of a single conical polymer nanopore, we find a pinched hysteresis of the current within a scanning frequency range of 0.01-0.1 Hz, switching to a diode below 0.01 Hz and a resistor above 0.1 Hz. We attribute the current hysteresis to the elastic strain at the tip side of the nanopore, caused by electrical force on the particles adsorbed at the inner wall surface. Our simulation and analytical equations match well with experimental results, with a phase diagram for predicting the system transitions. We demonstrate the plasticity of our nanofluidic memristor to be similar to a biological synapse. Our findings pave a new way for ionic neuromorphic computing using nanofluidic memristors.

Nanofluidic logic with mechano-ionic memristive switches

Neuromorphic systems are typically based on nanoscale electronic devices, but nature relies on ions for energy-efficient information processing. Nanofluidic memristive devices could thus potentially be used to construct electrolytic computers that mimic the brain down to its basic principles of operation. Here we report a nanofluidic device that is designed for circuit-scale in-memory processing. The device, which is fabricated using a scalable process, combines single-digit nanometric confinement and large entrance asymmetry and operates on the second timescale with a conductance ratio in the range of 9 to 60. In operando optical microscopy shows that the memory capabilities are due to the reversible formation of liquid blisters that modulate the conductance of the device. We use these mechano-ionic memristive switches to assemble logic circuits composed of two interactive devices and an ohmic resistor.

Electrochemical impedance spectroscopy of membranes with nanofluidic conical pores

Electrochemical impedance spectroscopy (EIS) constitutes a useful tool in membrane science and technology because it provides valuable structural and functional information. The different arcs observed in the impedance spectra permit to decouple and understand distinct physico-chemical phenomena occurring under operating conditions. By using EIS techniques, we have characterized here multipore asymmetric membranes with conical pores that exhibit a broad range of ionic conduction properties, including current rectification. These properties can be modulated by tuning the electrical interaction between the charges functionalized on the pore surface and the nanoconfined ionic solution. In particular, the membrane electrical response is studied as a function of the amplitude and frequency of the external voltage signal, the electrolyte type and concentration, and the solution pH. Remarkably, significant chemical inductance effects are observed. The scalability and biocompatibility of these pores suggest good potential for use in hybrid biodevices and interfaces.

Ionic Transistors

Biological voltage-gated ion channels, which behave as life's transistors, regulate ion transport precisely and selectively through atomic-scale selectivity filters to sustain important life activities. By this inspiration, voltage-adaptable ionic transistors that use ions as signal carriers may provide an alternative information processing unit beyond solid-state electronic devices. This review provides a comprehensive overview of the first generation of biomimetic ionic transistors, including their operating mechanisms, device architecture development, and property characterizations. Despite its infancy, significant progress has been made in the applications of ionic transistors in fields such as DNA detection, drug delivery, and ionic circuits. Challenges and prospects of full exploitation of ionic transistors for a broad spectrum of practical applications are also discussed.

Neuromorphic responses of nanofluidic memristors in symmetric and asymmetric ionic solutions

We show that ionic conduction properties of a multipore nanofluidic memristor can be controlled not only by the amplitude and frequency of an external driving signal but also by chemical gating based on the electrolyte concentration, presence of divalent and trivalent cations, and multi-ionic systems in single and mixed electrolytes. In addition, we describe the modulation of current rectification and hysteresis phenomena, together with neuromorphic conductance responses to voltage pulses, in symmetric and asymmetric external solutions. In our case, memristor conical pores act as nanofluidic diodes modulated by ionic solution characteristics due to the surface charge-regulated ionic transport. The above facts suggest potential sensing and actuating applications based on the conversion between ionic and electronic signals in bioelectrochemical hybrid circuits.

Synaptical Tunability of Multipore Nanofluidic Memristors

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.

A superstable, flexible, and scalable nanofluidic ion regulation composite membrane

Two-dimensional layered membranes with high and stable ion transport properties have various applications in nanofluidic devices; however, their construction remains a considerable challenge. Herein, we develop a superstable aramid nanofiber/graphite composite membrane with numerous one-dimensional and two-dimensional nano-confined interspaces for ultrafast ion transport. The fabricated flexible and scalable membrane exhibits high tensile strength (∼115.3 MPa) even after immersion in water for 90 days. Further, the aramid nanofiber/graphite conductor features the surface-charge-governed ion transport behavior. The ionic conductivity of the membrane at a low potassium chloride concentration of 10-4 mol/L can be enhanced by 16 times that of the bulk counterpart. More importantly, its structure and ionic conductivity remain unchanged even after immersion in different harsh solutions (e.g., acid, base, and ethanol) for over 30 days. Molecular dynamics simulations reveal that the superstability of the membrane is attributable to the robust interchain interactions within the aramid nanofibers and the strong interfacial interactions between the aramid nanofibers and graphite nanosheets. This study highlights the superior structural stability of the proposed flexible and scalable aramid nanofiber/graphite composite membrane, which could be employed in advanced nanofluidic devices for application under extreme working environments.

Iontronic Nanopore Model for Artificial Neurons: The Requisites of Spiking

Brain-inspired neuromorphic computing is currently being investigated for effective artificial intelligence (AI) systems. The development of artificial neurons and synapses is imperative to creating efficient computational biomimetic networks. Here we propose the minimal configuration of an effective iontronic spiking neuron based on a conical nanofluidic pore ionic diode. The conductance is composed of a Boltzmann open channel probability and a blocking inactivation function, forming the structure of a memristor. The presence of a negative resistance and the combination of activation-deactivation dynamics cause a Hopf bifurcation. Using the characteristic frequencies of small perturbation impedance spectroscopy, we discuss the conditions of spiking, in which the system enters a limit cycle oscillation. We arrive at the conclusion that an excitable neuron-like system can be made with a single active channel instead of the more complex combination of multiple channels that occurs in the Hodgkin-Huxley neuron model.