Long-term memory and synapse-like dynamics in two-dimensional nanofluidic channels

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.

Ion current oscillation of polyelectrolyte modified micropipettes

Here, we report for the first time that ion current oscillation (ICO) with periodic amplitude and frequency can autonomously occur at polyimidazole brush (PvimB) modified pipettes in an asymmetric solution with a pH gradient (e.g. pH 6.0/pH 8.0). Experimental results demonstrated that under a strong bias voltage, the proton responsive PvimB-modified pipettes exhibited significant current switching behavior under negative bias voltages, which contributed to the periodic oscillating ion current under constant biases. Based on this dynamic, the frequency and amplitude of the ICO phenomenon were regulated by adjusting the pH gradient in the asymmetric solution. ICOs under different bias voltages were further explored to show the voltage-dependent nature of this phenomenon. This observation of ICO phenomena offers a new strategy for designing iontronic devices with dynamic conductivity changes induced by surface chemical interactions within spatial confinements.

Electric Field Mediated Unclogging of Angstrom-Scale Channels

Angstrom-scale fluidic channels offer immense potential for applications in areas such as desalination, molecular sieving, biomolecular sequencing, and dialysis. Inspired by biological ion channels, nano- and angstrom (Å)-scale channels are fabricated that mimic these molecular or atomic-scale dimensions. At the Å-scale, these channels exhibit unique phenomena, including selective ion transport, osmotic energy generation, fast water and gas flows, and neuromorphic ion memory. However, practical utilization of Å-scale channels is often hindered by contamination, which can clog these nanochannels. In this context, a promising technique is introduced here for unclogging 2D channels, particularly those with sub-nanometre dimensions (≈6.8 Å). The voltage-cycling method emerges as an efficient and reliable solution for this challenge. The electric field effectively dislodges contaminants from the clogged Å-scale channels, facilitating ion and molecular transport. This study provides practical guidelines for reviving clogged nano- and Å-scale channels, thereby enhancing their applicability in various ion and molecular transport applications.

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.

An Aquatic Autonomic Nervous System

Reproducing human nervous systems with endogenous mechanisms has attracted increasing attention, driven by its great potential in streamlining the neuro-electronic interfaces with bilateral signaling. Here, an artificial aquatic autonomic nervous system (ANS) with switchable excitatory/inhibitory characteristics and acetylcholine (ACh)-mediated plasticity is reported based on the newly emerged organic photoelectrochemical transistor (OPECT). Under the modulation of spatial light and ACh, the system exhibits an immediate switch between excitation and inhibition, and many pulse patterns as well as advanced ANS functions are mimicked. To demonstrate its potential usage, the artificial ANS is then utilized to control artificial pupils and muscles to emulate real biological responses during an emergency. In contrast to previous solid-state attempts, this ANS is aqueous compatible just like biological nervous systems, which are capable of real neurotransmitter mediation.

Metal-Organic Framework Nanofluidic Synapse

Chemical synapse completes the signaling through neurotransmitter-mediated ion flux, the emulation of which has been a long-standing obstacle in neuromorphic exploration. Here, we report metal-organic framework (MOF) nanofluidic synapses in which conjugated MOFs with abundant ionic storage sites underlie the ionic hysteresis and simultaneously serve as catalase mimetics that sensitively respond to neurotransmitter glutamate (Glu). Various neurosynaptic patterns with adaptable weights are realized via Glu-mediated chemical/ionic coupling. In particular, nonlinear Hebbian and anti-Hebbian learning in millisecond time ranges are achieved, akin to those of chemical synapses. Reversible biochemical in-memory encoding via enzymatic Glu clearance is also accomplished. Such results are prerequisites for highly bionic electrolytic computers.

Constructing a supercapacitor-memristor through non-linear ion transport in MOF nanochannels

The coexistence and coupling of capacitive and memristive effects have been an important subject of scientific interest. While the capacitive effect in memristors has been extensively studied, the reciprocal scenario of the memristive effect in capacitors remains unexplored. In this study, we introduce a supercapacitor-memristor (CAPistor) concept, which is constructed by leveraging non-linear ion transport within the pores of a metal-organic framework zeolitic-imidazolate framework (ZIF-7). Within the nanochannels of the ZIF-7 electrode in an aqueous pseudocapacitor, the anionic species (OH-) of the electrolyte can be enriched and dissipated in different voltage regimes. This difference leads to a hysteresis effect in ion conductivity, constituting a memristive behavior in the pseudocapacitor. Thus, the pseudocapacitor-converted CAPistor seamlessly integrates the programmable resistance and memory functions of an ionic memristor into a supercapacitor, demonstrating enormous potential to extend the traditional energy storage applications of supercapacitors into emerging fields, including biomimetic nanofluidic ionics and neuromorphic computing.

Ion Coupling, Bonding, and Transfer in Narrow Carbon Nanotubes

Narrow carbon nanotubes (nCNT) are unique mimics of biological channels with water-ion selectivity attractive for applications such as water purification and osmotic energy harvesting, yet their understanding is still incomplete. Here, an ab initio computation is employed to develop the full picture of ion transfer in nCNT including specificity and coupling between ions. The thermodynamic costs of ion transfer are computed for single ions and ion pairs and used to evaluate different local coupling scenarios including strong (pairing) and weak (free-ion) coupling as well as "electroneutrality breakdown" (EB), possible for cations only due to their chemisorption-like interaction with nCNT. The results also indicate that nCNT behaves as a highly polarizable metal-like shell, which eliminates the dielectric energy when CNT accommodates coupled cation and anion. This allows facile computation and comparison of the full transfer costs, including translation entropy, for different ions in different coupling modes to identify the dominant regime. EB transfer appears most favorable for K+, while anions strongly favor transfer as pairs, except for chloride which favors weak coupling and, at neutral pH, transfers as a trace ion coupled to both cation and OH-. The results demonstrate that, in general, observed ion permeation and conduction in nCNT, especially for anions, reflect a complex ion-specific and composition-dependent interplay between different ions.

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.

A Photoelectrochemical Retinomorphic Synapse

Reproducing human visual functions with artificial devices is a long-standing goal of the neuromorphic domain. However, emulating the chemical language communication of the visual system in fluids remains a grand challenge. Here, a "multi-color" hydrogel-based photoelectrochemical retinomorphic synapse is reported with unique chemical-ionic-electrical signaling in an aqueous electrolyte that enables, e.g., color perception and biomolecule-mediated synaptic plasticity. Based on the specific enzyme-catalyzed chromogenic reactions, three multifunctional colored hydrogels are developed, which can not only synergize with the Bi2S3 photogate to recognize the primary colors but also synergize with a given polymeric channel to promote the long-term memory of the system. A synaptic array is further constructed for sensing color images and biomolecule-coded information communication. Taking advantage of the versatile biochemistry, the biochemical-driven reversible photoelectric response of the cone cell is further mimicked. This work introduces rich chemical designs into retinomorphic devices, providing a perspective for replicating the human visual system in fluids.

Soft Memristor at a Microbubble Interface

Memristors show promising features for neuromorphic computing. Here we report a soft memristor based on the liquid-vapor surface of a microbubble. The thickness of the liquid film was modulated by electrostatic and interfacial forces, enabling resistance switches. We found a pinched current hysteresis at scanning periods between 1.6 and 51.2 s, while representing a resistor below 1.6 s and a diode-like behavior above 51.2 s. We approximate the thickening/thinning dynamics of liquid film by pressure-driven flow at the interface and derived the impacts of salt concentration and voltage amplitude on the memory effects. Our work opens a new approach to building nanofluidic memristors by a soft interface, which may be useful for new types of neuromorphic computing in the future.

Investigation of Operational Parameters for Nanoelectrokinetic Purification and Preconcentration

This work reports on experimental investigations into the operational parameters of nanoelectrokinetic purification and preconcentration, especially utilizing on ion concentration polarization (ICP). ICP as a nanoscale electrokinetic phenomenon has demonstrated promising advances in various fields utilizing an ion depletion zone (IDZ) with a steep electric field gradient inside the ICP layer. However, the inevitable electrokinetic instability occurring within the IDZ has posed a challenge in operating the ICP system stably. To address the need for a stable and efficient ICP operation in various devices and applications, we propose an operational strategy along with conducted research to determine optimal operating ranges. In order to investigate the operational parameters, a unit voltage (VTH) is introduced as the threshold for initiating ICP. We examined the applicability of VTH across various operating ranges to ensure its effectiveness and versatility. In ICP purification, we categorize three modes (steady, burst, and unsteady) based on IDZ expansion and stability under varying VTH and flow rate conditions, presenting optimal operational conditions that minimize the voltage margin. In ICP preconcentration, a systematic investigation is conducted to observe the influence of background electrolyte concentration and voltage conditions on preconcentration efficiency, offering insights into the correlation between preconcentration factor, electrical conditions, and preconcentration time. Therefore, this research would contribute to the practical understanding of nanoelectrokinetics, providing insight into experimental designs. These findings are expected to offer valuable guidance to researchers aiming to utilize ICP's potential across a spectrum of applications, from purification to preconcentration, in the realm of micro/nanofluidic systems.

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.

Photoinduced Charge Injection from Shallow Point Defects in Diamond into Water

Thanks to its low or negative surface electron affinity and chemical inertness, diamond is attracting broad attention as a source material of solvated electrons produced by optical excitation of the solid-liquid interface. Unfortunately, its wide bandgap typically imposes the use of wavelengths in the ultraviolet range, hence complicating practical applications. Here, we probe the photocurrent response of water surrounded by single-crystal diamond surfaces engineered to host shallow nitrogen-vacancy (NV) centers. We observe clear signatures of diamond-induced photocurrent generation throughout the visible range and for wavelengths reaching up to 594 nm. Experiments as a function of laser power suggest that NV centers and other coexisting defects─likely in the form of surface traps─contribute to carrier injection, though we find that NVs dominate the system response in the limit of high illumination intensities. Given our growing understanding of near-surface NV centers and adjacent point defects, these results open new perspectives in the application of diamond-liquid interfaces to photocarrier-initiated chemical and spin processes in fluids.

A nanofluidic spiking synapse

Currently, the nanofluidic synapse can only perform basic neuromorphic pulse patterns. One immediate problem that needs to be addressed to further its capability of brain-like computing is the realization of a nanofluidic spiking device. Here, we report the use of a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate membrane to achieve bionic ionic current-induced spiking. In addition to the simulation of various electrical pulse patterns, our synapse could produce transmembrane ionic current-induced spiking, which is highly analogous to biological action potentials with similar phases and excitability. Moreover, the spiking properties could be modulated by ions and neurochemicals. We expect that this work could contribute to biomimetic spiking computing in solution.

Memristive Characteristics in an Asymmetrically Charged Nanochannel

The emergent nanofluidic memristor provides a promising way of emulating neuromorphic functions in the brain. The conical-shaped nanopore showed promising features for a nanofluidic memristor, inspiring us to investigate the memory effects in asymmetrically charged nanochannels due to their high current rectification, which may result in good memory effects. Here, the memory effects of an asymmetrically charged nanofluidic channel were numerically simulated by Poisson-Nernst-Planck equations. Our results showed that the I-V curves represented a diode in low scanning frequency and then became a memristor and finally a resistor as frequency increased. We successfully replicated the learning behavior in our system with history-dependent ion redistribution in the nanochannel. Some critical factors were quantitatively analyzed for the memory effects including voltage amplitude, optimal frequency, and Dukhin number. Experimental characterizations were also carried out. Our findings are useful for the design of nanofluidic memristors by the principle of enrichment and depletion as well as the determination of the best memory settings.

Tuning Interfacial Water Friction through Moiré Twist

Foundations of nanofluidics can enable advances in diverse applications such as water desalination, energy harvesting, and biological analysis. Dynamically manipulating nanofluidic properties, such as diffusion and friction, is an area of great scientific interest. Twisted bilayer graphene, particularly at the magic angle, has garnered attention for its unconventional superconductivity and correlated insulator behavior due to strong electronic correlations. The impact of the electronic properties of moiré patterns in twisted bilayer graphene on structural and dynamic properties of water remains largely unexplored. Computational challenges, stemming from simulating large unit cells using density functional theory, have hindered progress. This study addresses this gap by investigating water behavior on twisted bilayer graphene, employing a deep neural network potential (DP) model trained with a data set from ab initio molecular dynamics simulations. It is found that as the twisted angle approaches the magic angle, interfacial water friction increases, leading to a reduced water diffusion. Notably, the analysis shows that at smaller twisted angles with larger moiré patterns, water is more likely to reside in AA stacking regions than AB (or BA) stacking regions, a distinction that diminishes with smaller moiré patterns. This study illustrates the potential for leveraging the distinctive properties of moiré systems to effectively control and optimize interfacial fluid behavior.

Ion-Fluid Transport-Control Feedback along Nanopore Networks

Biological signaling correlates with the interrelation between ion and nanofluidic transportation pathways. However, artificial embodies with reconfigurable ion-fluid transport interaction aspects remain largely elusive. Herein, we unveiled an intimate interplay between nanopore-driven advancing flow and ion carriage for the spontaneous imbibition of aqueous solutions at the nanoporous thin film level. Ionic factors dominate transport phenomena processing and integration (ions influence fluid motion, which in turn governs the self-regulated ion traveling). We show an ion-induced translation effect that finely converts a chemical input, the nature of ions, into a related fluidic output: modulation of the extent of imbibition. We further find complex imbibition dynamics induced by the ion type and population. We peculiarly pinpoint a stop-and-go effective transport process with a programmable delay time triggered by selective guest-host interactions. The ion-fluid transport interplay is captured by a simple model that considers the counterbalance between the capillary infiltration and solution concentration, owing to water loss at the nanoporous film-air interface. Our results demonstrate that nanopore networks present fresh scenarios for understanding and controlling autonomous macroscopic liquid locomotion and offer a distinctive working principle for smart ion operation.

Geometrically Scalable Iontronic Memristors: Employing Bipolar Polyelectrolyte Gels for Neuromorphic Systems

Iontronics that are capable of mimicking the functionality of biological systems within an artificial fluidic network have long been pursued for biomedical applications and ion-based intelligence systems. Here, we report on facile and robust realization of iontronic bipolar memristors featuring a three-layer polyelectrolyte gel structure. Significant memristive hysteresis of ion currents was successfully accomplished, and the memory time proved geometrically scalable from 200 to 4000 s. These characteristics were enabled by the ion concentration polarization-induced rectification ratio within the polyelectrolyte gels. The memristors exhibited memory dynamics akin to those observed in unipolar devices, while the bipolar structure notably enabled prolonged memory time and enhanced the ion conductance switching ratio with mesoscale (10-1000 μm) geometry precision. These properties endow the devices with the capability of effective neuromorphic processing with pulse-based input voltage signals. Owing to their simple fabrication process and superior memristive performance, the presented iontronic bipolar memristors are versatile and can be easily integrated into small-scale iontronic circuits, thereby facilitating advanced neuromorphic computing functionalities.

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.

Formation of compounds with diverse polyelectrolyte morphologies and nonlinear ion conductance in a two-dimensional nanofluidic channel

Aqueous electrolytes subjected to angstrom-scale confinement have recently attracted increasing interest because of their distinctive structural and transport properties, as well as their promising applicability in bioinspired nanofluidic iontronics and ion batteries. Here, we performed microsecond-scale molecular dynamics simulations, which provided evidence of nonlinear ionic conductance under an external lateral electric field due to the self-assembly of cations and anions with diverse polyelectrolyte morphologies (e.g., extremely large ion clusters) in aqueous solutions within angstrom-scale slits. Specifically, we found that the cations and anions of Li2SO4 and CaSO4 formed chain-like polyelectrolyte structures, whereas those of Na2SO4 and MgSO4 predominantly formed a monolayer of hydrated salt. Additionally, the cations and anions of K2SO4 assembled into a hexagonal anhydrous ionic crystal. These ion-dependent diverse polyelectrolyte morphologies stemmed from the enhanced Coulomb interactions, weakened hydration and steric constraints within the angstrom-scale slits. More importantly, once the monolayer hydrated salt or ionic crystal structure was formed, the field-induced ion current exhibited an intriguing gating effect at a low field strength. This abnormal ion transport was attributed to the concerted movement of cations and anions within the solid polyelectrolytes, leading to the suppression of ion currents. When the electric field exceeded a critical strength, however, the ion current surged rapidly due to the dissolution of many cations and anions within a few nanoseconds in the aqueous solution.

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.

Negative Differential Resistance in Conical Nanopore Iontronic Memristors

Emerging ion transport dynamics with memory effects at nanoscale solution-substrate interfaces offers a unique opportunity to overcome the bottlenecks in traditional computational architectures, trade-offs in selectivity and throughput in separation, and electrochemical energy conversions. Negative differential resistance (NDR), a decrease in conductance with increasing potential, constitutes a new function from the perspective of time-dependent instead of steady-state nanoscale electrokinetic ion transport but remains unexplored in ionotronics to develop higher-order complexity and advanced capabilities. Herein, NDR is introduced in hysteretic and rectified ion transport through single conical nanopipettes (NPs) as ionic memristors. Deterministic and chaotic behaviors are controlled via an electric field as the sole stimulus. The NDR arises fundamentally from the availability and redistribution of the ionic charges during the hysteretic and rectified transport at asymmetric nanointerfaces. The elucidated mechanism is generalizable, and the drastically simplified operations enable tunable state-switching dynamics with higher-order complexity besides the first-order synaptic functions in multiple excitatory and inhibitory states.

Proton Coulomb Blockade Effect Involving Covalent Oxygen-Hydrogen Bond Switching

Instead of the canonical Grotthuss mechanism, we show that a knock-on proton transport process is preferred between organic functional groups (e.g., -COOH and -OH) and adjacent water molecules in biological proton channel and synthetic nanopores through comprehensive quantum and classical molecular dynamics simulations. The knock-on process is accomplished by the switching of covalent O─H bonds of the functional group under externally applied electric fields. The proton transport through the synthetic nanopore exhibits nonlinear current-voltage characteristics, suggesting an unprecedented proton Coulomb blockade effect. These findings not only enhance the understanding of proton transport in nanoconfined systems but also pave the way for the design of a variety of proton-based nanofluidic devices.

Brain-inspired computing with fluidic iontronic nanochannels

The brain's remarkable and efficient information processing capability is driving research into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels are emerging as an exciting platform for neuromorphic computing, representing a departure from conventional solid-state devices by directly mimicking the brain's fluidic ion transport. Supported by a quantitative theoretical model, we present easy-to-fabricate tapered microchannels that embed a conducting network of fluidic nanochannels between a colloidal structure. Due to transient salt concentration polarization, our devices are volatile memristors (memory resistors) that are remarkably stable. The voltage-driven net salt flux and accumulation, that underpin the concentration polarization, surprisingly combine into a diffusionlike quadratic dependence of the memory retention time on the channel length, allowing channel design for a specific timescale. We implement our device as a synaptic element for neuromorphic reservoir computing. Individual channels distinguish various time series, that together represent (handwritten) numbers, for subsequent in silico classification with a simple readout function. Our results represent a significant step toward realizing the promise of fluidic ion channels as a platform to emulate the rich aqueous dynamics of the brain.

Soft Nanofluidic Machinery

Soft devices integrating flexible structures and versatile material functionalities offer platform technologies for the healthcare, information, and communication industries. The flexibility can be achieved by constructing devices from low-dimensional nanostructures or nanoporous soft materials. By pushing the limits of fabrication and structuring down to the nanometer and Ångstrom scales, nanofluidics with extreme spatial confinement has recently been actively explored for energy-, environment-, and human-friendly device applications as alternative solutions to electronics and mechanotronics. Soft nanofluidic machinery enables ultrafast and selective fluidic transport, efficient energy conversion, and information processing, offering unconventional dimensions of design. The physics behind the design is introduced, followed by discussions on their implementations and performance and an outlook on the opportunities and challenges.

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.

Enhanced Rectification Performance in Bipolar Janus Graphene Oxide Channels by Lateral Electric Fields

Improving the ionic rectification in nanochannels enables versatile applications such as biosensors, energy harvesting, and fluidic diodes. While previous work mostly focused on the effect of channel geometry and surface charge, in this work via a series of molecular dynamics simulations, we find a striking phenomenon that the ionic current rectification (ICR) ratio in Janus graphene oxide (GO) channels can be tremendously promoted by lateral electric fields. First, under a given axial electric field, an additional lateral electric field can improve the ICR ratio by several times to an order, depending on the channel symmetry. The symmetric channel has an obviously greater ICR ratio because it maintains a more pronounced ion transport disparity at opposite axial fields. The underlying mechanism for the function of the lateral electric field is that it promotes the lateral migration of ions and thus amplifies the ion-residue electrostatic interaction at opposite axial fields, enlarging the ion dynamical difference. Furthermore, for different axial electric fields, the ICR ratio can always be improved by lateral electric fields (up to two orders), suggesting that the ICR improvement is universal. Our results demonstrate that applying a lateral electric field could be a new method to improve the rectification performance of nanochannels, providing valuable guidance for the design of efficient ionic diode devices.

Artificial Neural Synapses Based on Microfluidic Memristors Prepared by Capillary Tubes and Ionic Liquid

Neuromorphic simulation, i.e., the use of electronic devices to simulate the neural networks of the human brain, has attracted a lot of interest in the fields of data processing and memory. This work provides a new method for preparing a 1,3-dimethylimidazolium nitrate ([MMIm][NO3]:H2O) microfluidic memristor that is ultralow cost and technically uncomplicated. Such a fluidic device uses capillaries as memory tubes, which are structurally similar to interconnected neurons by simple solution treatment. When voltage is applied, the transmission of anions and cations in the tube corresponds to the release of neurotransmitters from the presynaptic membrane to the postsynaptic membrane. The change of synaptic weights (plasticity) also can be simulated by the gradual change of conductance of the fluid memristor. The learning process of microfluidic memristors is very obvious, and the habituation and recovery behaviors they exhibit are extremely similar to biological activities, representing its good use for simulating neural synapses.

Bioinspired 2D nanofluidic membranes for energy applications

Bioinspired two-dimensional (2D) nanofluidic membranes have been explored for the creation of high-performance ion transport systems that can mimic the delicate transport functions of living organisms. Advanced energy devices made from these membranes show excellent energy storage and conversion capabilities. Further research and development in this area are essential to unlock the full potential of energy devices and facilitate the development of high-performance equipment toward real-world applications and a sustainable future. However, there has been minimal review and summarization of 2D nanofluidic membranes in recent years. Thus, it is necessary to carry out an extensive review to provide a survey library for researchers in related fields. In this review, the classification and the raw materials that are used to construct 2D nanofluidic membranes are first presented. Second, the top-down and bottom-up methods for constructing 2D membranes are introduced. Next, the applications of bioinspired 2D membranes in osmotic energy, hydraulic energy, mechanical energy, photoelectric conversion, lithium batteries, and flow batteries are discussed in detail. Finally, the opportunities and challenges that 2D nanofluidic membranes are likely to face in the future are envisioned. This review aims to provide a broad knowledge base for constructing high-performance bioinspired 2D nanofluidic membranes for advanced energy applications.

Pseudocapacitance-Induced Synaptic Plasticity of Tribo-Phototronic Effect Between Ionic Liquid and 2D MoS2

Contact-induced electrification, commonly referred to as triboelectrification, is the subject of extensive investigation at liquid-solid interfaces due to its wide range of applications in electrochemistry, energy harvesting, and sensors. This study examines the triboelectric between an ionic liquid and 2D MoS2 under light illumination. Notably, when a liquid droplet slides across the MoS2 surface, an increase in the generated current and voltage is observed in the forward direction, while a decrease is observed in the reverse direction. This suggests a memory-like tribo-phototronic effect between ionic liquid and 2D MoS2 . The underlying mechanism behind this tribo-phototronic synaptic plasticity is proposed to be ion adsorption/desorption processes resulting from pseudocapacitive deionization/ionization at the liquid-MoS2  interface. This explanation is supported by the equivalent electrical circuit modeling, contact angle measurements, and electronic band diagrams. Furthermore, the influence of various factors such as velocity, step size, light wavelength and intensity, ion concentration, and bias voltage is thoroughly investigated. The artificial synaptic plasticity arising from this phenomenon exhibits significant synaptic features, including potentiation/inhibition, paired-pulse facilitation/depression, and short-term memory (STM) to long-term memory (LTM) transition. This research opens up promising avenues for the development of synaptic memory systems and intelligent sensing applications based on liquid-solid interfaces.

Dielectric Detection of Single Nanoparticles Using a Microwave Resonator Integrated with a Nanopore

The characterization of individual nanoparticles in a liquid constitutes a critical challenge for the environmental, material, and biological sciences. To detect nanoparticles, electronic approaches are especially desirable owing to their compactness and lower costs. While electronic detection in the form of resistive-pulse sensing has enabled the acquisition of geometric properties of various analytes, impedimetric measurements to obtain dielectric signatures of nanoparticles have scarcely been reported. To explore this orthogonal sensing modality, we developed an impedimetric sensor based on a microwave resonator with a nanoscale sensing gap surrounding a nanopore built on a 220 nm silicon nitride membrane. The microwave resonator has a coplanar waveguide configuration with a resonance frequency of approximately 6.6 GHz. The approach of single nanoparticles near the sensing region and their translocation through the nanopores induced sudden changes in the impedance of the structure. The impedance changes, in turn, were picked up by the phase response of the microwave resonator. We worked with 100 and 50 nm polystyrene nanoparticles to observe single-particle events. Our current implementation was limited by the nonuniform electric field at the sensing region. This work provides a complementary sensing modality for nanoparticle characterization, where the dielectric response, rather than ionic current, determines the signal.

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity

Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.

Aqueous chemimemristor based on proton-permeable graphene membranes

Memristive devices, electrical elements whose resistance depends on the history of applied electrical signals, are leading candidates for future data storage and neuromorphic computing. Memristive devices typically rely on solid-state technology, while aqueous memristive devices are crucial for biology-related applications such as next-generation brain-machine interfaces. Here, we report a simple graphene-based aqueous memristive device with long-term and tunable memory regulated by reversible voltage-induced interfacial acid-base equilibria enabled by selective proton permeation through the graphene. Surface-specific vibrational spectroscopy verifies that the memory of the graphene resistivity arises from the hysteretic proton permeation through the graphene, apparent from the reorganization of interfacial water at the graphene/water interface. The proton permeation alters the surface charge density on the CaF2 substrate of the graphene, affecting graphene's electron mobility, and giving rise to synapse-like resistivity dynamics. The results pave the way for developing experimentally straightforward and conceptually simple aqueous electrolyte-based neuromorphic iontronics using two-dimensional (2D) materials.

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.

Memristive Response and Capacitive Spiking in Aqueous Ion Transport through Two-Dimensional Nanopore Arrays

In living organisms, information is processed in interconnected symphonies of ionic currents spiking through protein ion channels. As a result of dynamic switching of their conductive states, ion channels exhibit a variety of current-voltage nonlinearities and memory effects. Fueled by the promise of computing architectures entirely different from von Neumann, recent attempts to identify and harness similar phenomena in artificial nanofluidic environments focused on demonstrating analogue circuit elements with memory. Here we explore aqueous ionic transport through two-dimensional (2D) membranes featuring arrays of ion-trapping crown-ether-like pores. We demonstrate that for aqueous salts featuring ions with different ion-pore binding affinities, memristive effects emerge through coupling between the time-delayed state of the system and its transport properties. We also demonstrate a nanopore array that behaves as a capacitor with a strain-tunable built-in barrier, yielding behaviors ranging from current spiking to an ohmic response. By focusing on the illustrative underlying mechanisms, we demonstrate that realistically observable memory effects may be achieved in nanofluidic systems featuring crown-porous 2D membranes.

Desalination Performance in Janus Graphene Oxide Channels: Geometric Asymmetry vs Charge Polarity

Improving the desalination performance of membranes is always in the spotlight of scientific research; however, Janus channels with polarized surface charge as nanofiltration membranes are still unexplored. In this work, using molecular dynamics simulations, we demonstrate that Janus graphene oxide (GO) channels with appropriate geometry and surface charge can serve as highly efficient nanofiltration membranes. We observe that the water permeability of symmetric Janus GO channels is significantly superior to that of asymmetric channels without sacrificing much ion rejection, owing to weakened ion blockage and electrostatic effects. Furthermore, in symmetric Janus GO channels, the transport of water and ions is sensitive to the charge polarity of the channel inner surface, which is realized by tuning the ratio of cationic and anionic functionalization. Specifically, with the increase in cationic functionalization, the water flux decreases monotonously, while ion rejection displays an interesting maximum behavior that indicates desalination optimization. Moreover, the trade-off between water permeability and ion rejection suggests that the Janus GO channels have an excellent desalination potential and are highly tunable according to the specific water treatment requirements. Our work sheds light on the key role of channel geometry and charge polarity in the desalination performance of Janus GO channels, which paves the way for the design of novel desalination devices.

Asymmetric rectified electric fields: nonlinearities and equivalent circuits

Recent experiments [S. H. Hashemi et al., Phys. Rev. Lett., 2018, 121, 185504] have shown that a long-ranged steady electric field emerges when applying an oscillating voltage over an electrolyte with unequal mobilities of cations and anions confined between two planar blocking electrodes. To explain this effect we analyse full numerical calculations based on the Poisson-Nernst-Planck equations by means of analytically constructed equivalent electric circuits. Surprisingly, the resulting equivalent circuit has two capacitive elements, rather than one, which introduces a new timescale for electrolyte dynamics. We find a good qualitative agreement between the numerical results and our simple analytic model, which shows that the long-range steady electric field emerges from the different charging rates of cations and anions in the electric double layers.

Surface-dominant micro/nanofluidics for efficient green energy conversion

Green energy conversion in aqueous systems has attracted considerable interest owing to the sustainable clean energy demand resulting from population and economic growth and urbanization, as well as the significant potential energy from water resources and other regenerative sources coupled with fluids. In particular, molecular motion based on intrinsic micro/nanofluidic phenomena at the liquid-solid interface (LSI) is crucial for efficient and sustainable green energy conversion. The electrical double layer is the main factor affecting transport, interaction between molecules and surfaces, non-uniform ion distribution, synthesis, stimulated reactions, and motion by external renewable resources in both closed nanoconfinement and open surfaces. In this review, we summarize the state-of-the-art progress in physical and chemical reaction-based green energy conversion in LSI, including nanoscale fabrication, key mechanisms, applications, and limitations for practical implementation. The prospects for resolving critical challenges in this field and inspiring other promising research areas in the infancy stage (studying chemical and biological dynamics at the single-molecule level and nanofluidic neuromorphic computing) are also discussed.

Fabrication of angstrom-scale two-dimensional channels for mass transport

Fluidic channels at atomic scales regulate cellular trafficking and molecular filtration across membranes, and thus play crucial roles in the functioning of living systems. However, constructing synthetic channels experimentally at these scales has been a significant challenge due to the limitations in nanofabrication techniques and the surface roughness of the commonly used materials. Angstrom (Å)-scale slit-like channels overcome such challenges as these are made with precise control over their dimensions and can be used to study the fluidic properties of gases, ions and water at unprecedented scales. Here we provide a detailed fabrication method of the two-dimensional Å-scale channel devices that can be assembled to contain a desired number of channels, a single channel or up to hundreds of channels, made with atomic-scale precision using layered crystals. The procedure includes the fabrication of the substrate, flake, spacer layer, flake transfers, van der Waals assembly and postprocessing. We further explain how to perform molecular transport measurements with the Å-channels to directly probe the intriguing and anomalous phenomena that help shed light on the physics governing ultra-confined transport. The procedure requires a total of 1-2 weeks for the fabrication of the two-dimensional channel device and is suitable for users with prior experience in clean room working environments and nanofabrication.

Ultralow Energy Consumption Angstrom-Fluidic Memristor

The emergence of nanofluidic memristors has made a giant leap to mimic the neuromorphic functions of biological neurons. Here, we report neuromorphic signaling using Angstrom-scale funnel-shaped channels with poly-l-lysine (PLL) assembled at nano-openings. We found frequency-dependent current-voltage characteristics under sweeping voltage, which represents a diode in low frequencies, but it showed pinched current hysteresis as frequency increases. The current hysteresis is strongly dependent on pH values but weakly dependent on salt concentration. We attributed the current hysteresis to the entropy barrier of PLL molecules entering and exiting the Angstrom channels, resulting in reversible voltage-gated open-close state transitions. We successfully emulated the synaptic adaptation of Hebbian learning using voltage spikes and obtained a minimum energy consumption of 2-23 fJ in each spike per channel. Our findings pave a new way to mimic neuronal functions by Angstrom channels in low energy consumption.

Hydrophobically gated memristive nanopores for neuromorphic applications

Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the electrical behaviour of memristors, resistors with memory. State-of-the-art technologies currently employ semiconductor-based neuromorphic approaches, which have already demonstrated their efficacy in machine learning systems. However, these approaches still cannot match performance achieved by biological neurons in terms of energy efficiency and size. In this study, we utilise molecular dynamics simulations, continuum models, and electrophysiological experiments to propose and realise a bioinspired hydrophobically gated memristive nanopore. Our findings indicate that hydrophobic gating enables memory through an electrowetting mechanism, and we establish simple design rules accordingly. Through the engineering of a biological nanopore, we successfully replicate the characteristic hysteresis cycles of a memristor and construct a synaptic device capable of learning and forgetting. This advancement offers a promising pathway for the realization of nanoscale, cost- and energy-effective, and adaptable bioinspired memristors.

Versatile Neuromorphic Modulation and Biosensing based on N-type Small-molecule Organic Mixed Ionic-Electronic Conductors

The ion/chemical-based modulation feature of organic mixed ionic-electronic conductors (OMIECs) are critical to advancing next generation bio-integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n-type fused all-acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR-Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state-of-art n-type OMIECs based OENS. These superior artificial synapse characteristics of gNR-Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n-type small-molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub-μM level in aqueous electrolyte. This work may open a new path of small-molecule ion-electron conductors for next-generation ANN and bioelectronics.

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.