Nanochannel-Based Transport in an Interfacial Memristor Can Emulate the Analog Weight Modulation of Synapses

Synaptic-like plasticity in 2D nanofluidic memristor from competitive bicationic transport

Synaptic plasticity, the dynamic tuning of signal transmission strength between neurons, serves as a fundamental basis for memory and learning in biological organisms. This adaptive nature of synapses is considered one of the key features contributing to the superior energy efficiency of the brain. Here, we use molecular dynamics simulations to demonstrate synaptic-like plasticity in a subnanoporous two-dimensional membrane. We show that a train of voltage spikes dynamically modifies the membrane's ionic permeability in a process involving competitive bicationic transport. This process is shown to be repeatable after a given resting period. Because of a combination of subnanometer pore size and the atomic thinness of the membrane, this system exhibits energy dissipation of 0.1 to 100 aJ per voltage spike, which is several orders of magnitude lower than 0.1 to 10 fJ per spike in the human synapse. We reveal the underlying physical mechanisms at molecular detail and investigate the local energetics underlying this apparent synaptic-like behavior.

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.

Nanopore-based sensors for DNA sequencing: a review

Nanopore sensors, owing to their distinctive structural properties, can be used to detect biomolecular translocation events. These sensors operate by monitoring variations in electric current amplitude and duration, thereby enabling the calibration and distinction of various biomolecules. As a result, nanopores emerge as a potentially powerful tool in the field of deoxyribonucleic acid (DNA) sequencing. However, the interplay between testing bandwidth and noise often leads to the loss of part of the critical translocation signals, presenting a substantial challenge for the precise measurement of biomolecules. In this context, innovative detection mechanisms have been developed, including optical detection, tunneling current detection, and nanopore field-effect transistor (FET) detection. These novel detection methods are based on but beyond traditional nanopore techniques and each of them has unique advantages. Notably, nanopore FET sensors stand out for their high signal-to-noise ratio (SNR) and high bandwidth measurement capabilities, overcoming the limitations typically associated with traditional solid-state nanopore (SSN) technologies and thus paving the way for new avenues to biomolecule detection. This review begins by elucidating the fundamental detection principles, development history, applications, and fabrication methods for traditional SSNs. It then introduces three novel detection mechanisms, with a particular emphasis on nanopore FET detection. Finally, a comprehensive analysis of the advantages and challenges associated with both SSNs and nanopore FET sensors is performed, and then insights into the future development trajectories for nanopore FET sensors in DNA sequencing are provided. This review has two main purposes: firstly, to provide researchers with a preliminary understanding of advancements in the nanopore field, and secondly, to offer a comprehensive analysis of the fabrication techniques, transverse current detection principles, challenges, and future development trends in the field of nanopore FET sensors. This comprehensive analysis aims to help give researchers in-depth insights into cutting-edge advancements in the field of nanopore FET sensors.

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.

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.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

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.

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.

Conformational transitions in redissolved silk fibroin films and application for printable self-powered multistate resistive memory biomaterials

3D printing of water stable proteins with elastic properties offers a broad range of applications including self-powered biomedical devices driven by piezoelectric biomaterials. Here, we present a study on water-soluble silk fibroin (SF) films. These films were prepared by mixing degummed silk fibers and calcium chloride (CaCl2) in formic acid, resulting in a silk I-like conformation, which was then converted into silk II by redissolving in phosphate buffer (PBS). Circular dichroism, Raman and infrared (IR) spectroscopies were used to investigate the transitions of secondary structure in silk I and silk II as the pH of the solvent and the sonication time were changed. We showed that a solvent with low pH (e.g. 4) maintains the silk I β-turn structure; in contrast solvent with higher pH (e.g. 7.4) promotes β-sheet features of silk II. Ultrasonic treatment facilitates the transition to water stable silk II only for the SF redissolved in PBS. SF from pH 7.4 solution has been printed using extrusion-based 3D printing. A self-powered memristor was realized, comprising an SF-based electric generator and an SF 3D-printed memristive unit connected in series. By exploiting the piezoelectric properties of silk II with higher β-sheet content and Ca2+ ion transport phenomena, the application of an input voltage driven by a SF generator to SF 3D printed holey structures induces a variation from an initial low resistance state (LRS) to a high resistance state (HRS) that recovers in a few minutes, mimicking the transient memory, also known as short-term memory. Thanks to this holistic approach, these findings can contribute to the development of self-powered neuromorphic networks based on biomaterials with memory capabilities.

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.

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.

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.

Synapse Neurotransmitter Channel-Inspired AlOx Memristor with "V" Type Oxygen Vacancy Distribution

Memristor possesses great potential and advantages in neuromorphic computing, while consistency and power consumption issues have been hindering its commercialization. Low cost and accuracy are the advantages of human brain, so memristors can be used to construct brain-like synaptic devices to solve these problems. In this work, a five-layer AlOx device with a V-shaped oxygen distribution is used to simulate biological synapses. The device simulates synapse structurally. Further, under electrical stimulation, O2- moves to the Ti electrode and oxygen vacancy (Vo) moves to the Pt electrode, thus forming a conductive filament (CF), which simulates the Ca2+ flow and releases neurotransmitters to the postsynaptic membrane, thus realizing the transmission of information. By controlling applied voltage, the regulation of Ca2+ gated pathway is realized to control the Ca2+ internal flow and achieve different degrees of information transmission. Long-term Potentiation (LTP)/Long-term Depression (LTD), Spike Timing Dependent Plasticity (STDP), these basic synaptic performances can be simulated. The AlOx device realizes low power consumption (56.7 pJ/392 fJ), high switching speed (25 ns/60 ns), and by adjusting the window value, the nonlinearity is improved (0.133/0.084), a high recognition accuracy (98.18%) is obtained in neuromorphic simulation. It shows a great prospect in multi-value storage and neuromorphic computing.

Interface engineering in ZnO/CdO hybrid nanocomposites to enhanced resistive switching memory for neuromorphic computing

Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C15ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/-0.2) voltage, a high RON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures.

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.

Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.

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.

Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for the manifestation of artificial intelligence and brain-machine interaction. Organic iontronic memristive materials (OIMs), which possess an organic backbone and exhibit stoichiometric ionic states, have emerged as pivotal contenders for the realization of high-performance bionic iontronic memristors. In this review, a comprehensive analysis of the progress and prospects of OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, and wide-ranging applications in memristive devices. Predictably, the field of OIMs, as a rapidly developing research subject, presents an exciting opportunity for the development of highly efficient neuro-iontronic systems in areas such as in-sensor computing devices, artificial synapses, and human perception.

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.

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.

Emulation of Pavlovian conditioning and pattern recognition through fully connected neural networks using Holmium oxide (Ho2O3) based synaptic RRAM device

In this manuscript, we report on the paramagnetic Ho2O3-based synaptic resistive random-access memory device for the implementation of neuronal functionalities such as long-term potentiation, long-term depression and spike timing dependent plasticity respectively. The plasticity of the artificial synapse is also studied by varying pulse amplitude, pulse width, and pulse interval. In addition, we could classify handwritten Modified National Institute of Standards and Technology data set (MNIST) using a fully connected neural network (FCN). The device-based FCN records a high classification accuracy of 93.47% which is comparable to the software-based test accuracy of 97.97%. This indicates the highly optimized behavior of our synaptic device for hardware neuromorphic applications. Successful emulation of Pavlovian classical conditioning for associative learning of the biological brain is achieved. We believe that the present device consists the potential to utilize in neuromorphic applications.

Cascade-heterogated biphasic gel iontronics for electronic-to-multi-ionic signal transmission

Currently, electronics and iontronics in abiotic-biotic systems can only use electrons and single-species ions as unitary signal carriers. Thus, a mechanism of gating transmission for multiple biosignals in such devices is needed to match and modulate complex aqueous-phase biological systems. Here we report the use of cascade-heterogated biphasic gel iontronics to achieve diverse electronic-to-multi-ionic signal transmission. The cascade-heterogated property determined the transfer free energy barriers experienced by ions and ionic hydration-dehydration states under an electric potential field, fundamentally enhancing the distinction of cross-interface transmission between different ions by several orders of magnitude. Such heterogated or chemical-heterogated iontronics with programmable features can be coupled with multi-ion cross-interface mobilities for hierarchical and selective cross-stage signal transmission. We expect that such iontronics would be ideal candidates for a variety of biotechnology applications.

Controllable Fabrication and Rectification of Bipolar Nanofluid Diodes in Funnel-Shaped Si3 N4 Nanopores

Solid-state nanopores attract widespread interest, owning to outstanding robustness, extensive material availability, as well as capability for flexible manufacturing. Bioinspired solid-state nanopores further emerge as potential nanofluidic diodes for mimicking the rectification progress of unidirectional ionic transport in biological K+ channels. However, challenges that remain in rectification are over-reliance on complicated surface modifications and limited control accuracy in size and morphology. In this study, suspended Si3 N4 films of only 100 nm thickness are used as substrate and funnel-shaped nanopores are controllably etched on that with single-nanometer precision, by focused ion beam (FIB) equipped with a flexibly programmable ion dose at any position. A small diameter 7 nm nanopore can be accurately and efficiently fabricated in only 20 ms and verified by a self-designed mathematical model. Without additional modification, funnel-shaped Si3 N4 nanopores functioned as bipolar nanofluidic diodes achieve high rectification by simply filling each side with acidic and basic solution, respectively. Main factors are finely tuned experimentally and simulatively to enhance the controllability. Moreover, nanopore arrays are efficiently prepared to further improve rectification performance, which has great potential for high-throughput practical applications such as extended release of drugs, nanofluidic logic systems, and sensing for environmental monitoring and clinical diagnosis.

Iontronic memories based on ionic redox systems: operation protocols

A recently developed, new ionic device called the ionic voltage effect soft triode (IVEST) was optimized, tuned and embedded into a memory application concept. The device is an electrochemical micro-cell, consisting of a top electrode and two bottom electrodes. The device controls the concentration and diffusion of ions via the voltage applied on the top electrode. The device showed a memory effect lasting up to 6 hours. Despite the remarkably large stability time, the memory contrast was small in the first device versions. Now, we have increased the memory contrast by introducing a new external electrical circuit layout combined with a new operation protocol. This new investigation also reveals peculiarities of the memory and shows that the IVEST can be used in memory applications. These iontronic memories show a secondary information storage connected with the read-out frequency.

Targeted Chemical Processing Initiating Biosome Action-Potential-Matched Artificial Synapses for the Brain-Machine Interface

A great gap still exists between artificial synapses and their biological counterparts in operation voltage or stimulation duration. Here, an artificial synaptic device based on a thin-film transistor with an operating voltage (-50-50 mV) analogous to biological action potential is developed by targeted chemical processing with the help of supercritical fluids. Chemical molecules [hexamethyldisilazane (HMDS)] are elaborately chosen and brought into the target interface to form charge receptors through supercritical processing. These charge receptors with the ability of capturing electrons mimic neurotransmitter receptors in terms of mechanism and constitute key players accounting for the synaptic behaviors. The relatively lower electrical barrier height contributes to an action-potential-matched operating voltage and considerably low power consumption (∼1 pJ/synaptic event), minimizing the divide with biological synapse for a seamless linkage to the biosystem or brain-machine interface. The stable synaptic behaviors also lead to near-ideal accuracy in pattern recognition. Moreover, this methodology that introduces chemical groups into a target interface can be viewed as a platform technology that could be adapted to other conventional devices with suitable chemical molecules to reach designed synaptic behaviors. This environmentally friendly and low-temperature processing method, which can be performed even after device fabrication, has the potential to play an important role in the future development of bionic devices.

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.

A Single-Entity Method for Actively Controlled Nucleation and High-Quality Protein Crystal Synthesis

Lack of controls and understanding in nucleation, which proceeds crystal growth and other phase transitions, has been a bottleneck challenge in chemistry, materials, biology, and other fields. The exemplary needs for better methods for biomacromolecule crystallization include (1) synthesizing crystals for high-resolution structure determinations in fundamental research and (2) tuning the crystal habit and thus the corresponding properties in materials and pharmaceutical applications. Herein, a deterministic method is established capable of sustaining the nucleation and growth of a single crystal using the protein lysozyme as a prototype. The supersaturation is localized at the interface between a sample and a precipitant solution, spatially confined by the tip of a single nanopipette. The exchange of matter between the two solutions determines the supersaturation, which is controlled by electrokinetic ion transport driven by an external potential waveform. Nucleation and subsequent crystal growth disrupt the ionic current limited by the nanotip and are detected. The nucleation and growth of individual single crystals are measured in real time. Electroanalytical and optical signatures are elucidated as feedbacks with which active controls in crystal quality and method consistency are achieved: five out of five crystals diffract at a true atomic resolution of up to 1.2 Å. As controls, those synthesized under less optimized conditions diffract poorly. The crystal habits during the growth process are tuned successfully by adjusting the flux. The universal mechanism of nano-transport kinetics, together with the correlations of the diffraction quality and crystal habit with the crystallization control parameters, lay the foundation for the generalization to other materials systems.

Learning from the Brain: Bioinspired Nanofluidics

The human brain completes intelligent behaviors such as the generation, transmission, and storage of neural signals by regulating the ionic conductivity of ion channels in neuron cells, which provides new inspiration for the development of ion-based brain-like intelligence. Against the backdrop of the gradual maturity of neuroscience, computer science, and micronano materials science, bioinspired nanofluidic iontronics, as an emerging interdisciplinary subject that focuses on the regulation of ionic conductivity of nanofluidic systems to realize brain-like functionalities, has attracted the attention of many researchers. This Perspective provides brief background information and the state-of-the-art progress of nanofluidic intelligent systems. Two main categories are included: nanofluidic transistors and nanofluidic memristors. The prospects of nanofluidic iontronics' interdisciplinary progress in future artificial intelligence fields such as neuromorphic computing or brain-computer interfaces are discussed. This Perspective aims to give readers a clear understanding of the concepts and prospects of this emerging interdisciplinary field.

Nanopores: synergy from DNA sequencing to industrial filtration - small holes with big impact

Nanopores in thin membranes play important roles in science and industry. Single nanopores have provided a step-change in portable DNA sequencing and understanding nanoscale transport while multipore membranes facilitate food processing and purification of water and medicine. Despite the unifying use of nanopores, the fields of single nanopores and multipore membranes differ - to varying degrees - in terms of materials, fabrication, analysis, and applications. Such a partial disconnect hinders scientific progress as important challenges are best resolved together. This Viewpoint suggests how synergistic crosstalk between the two fields can provide considerable mutual benefits in fundamental understanding and the development of advanced membranes. We first describe the main differences including the atomistic definition of single pores compared to the less defined conduits in multipore membranes. We then outline steps to improve communication between the two fields such as harmonizing measurements and modelling of transport and selectivity. The resulting insight is expected to improve the rational design of porous membranes. The Viewpoint concludes with an outlook of other developments that can be best achieved by collaboration across the two fields to advance the understanding of transport in nanopores and create next-generation porous membranes tailored for sensing, filtration, and other applications.

Neuromorphic functions with a polyelectrolyte-confined fluidic memristor

Reproducing ion channel-based neural functions with artificial fluidic systems has long been an aspirational goal for both neuromorphic computing and biomedical applications. In this study, neuromorphic functions were successfully accomplished with a polyelectrolyte-confined fluidic memristor (PFM), in which confined polyelectrolyte-ion interactions contributed to hysteretic ion transport, resulting in ion memory effects. Various electric pulse patterns were emulated by PFM with ultralow energy consumption. The fluidic property of PFM enabled the mimicking of chemical-regulated electric pulses. More importantly, chemical-electric signal transduction was implemented with a single PFM. With its structural similarity to ion channels, PFM is versatile and easily interfaces with biological systems, paving a way to building neuromorphic devices with advanced functions by introducing rich chemical designs.

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

Fine-tuned ion transport across nanoscale pores is key to many biological processes, including neurotransmission. Recent advances have enabled the confinement of water and ions to two dimensions, unveiling transport properties inaccessible at larger scales and triggering hopes of reproducing the ionic machinery of biological systems. Here we report experiments demonstrating the emergence of memory in the transport of aqueous electrolytes across (sub)nanoscale channels. We unveil two types of nanofluidic memristors depending on channel material and confinement, with memory ranging from minutes to hours. We explain how large time scales could emerge from interfacial processes such as ionic self-assembly or surface adsorption. Such behavior allowed us to implement Hebbian learning with nanofluidic systems. This result lays the foundation for biomimetic computations on aqueous electrolytic chips.

Directional Formation of Conductive Filaments for a Reliable Organic-Based Artificial Synapse by Doping Molybdenum Disulfide Quantum Dots into a Polymer Matrix

The conductive filament (CF) model, as an important means to realize synaptic functions, has received extensive attention and has been the subject of intense research. However, the random and uncontrollable growth of CFs seriously affects the performances of such devices. In this work, we prepared a neural synaptic device based on polyvinyl pyrrolidone-molybdenum disulfide quantum dot (MoS₂ QD) nanocomposites. The doping with MoS₂ QDs was found to control the growth mode of Ag CFs by providing active centers for the formation of Ag clusters, thus reducing the uncertainty surrounding the growth of Ag CFs. As a result, the device, with a low power consumption of 32.8 pJ/event, could be used to emulate a variety of synaptic functions, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation, post-tetanic potentiation, short-term memory to long-term memory conversion, and "learning experience" behavior. After having undergone consecutive stimulation with different numbers of pulses, the device stably realized a "multi-level LTP to LTD conversion" function. Moreover, the synaptic characteristics of the device experienced no degradation due to mechanical stress. Finally, the simulation result based on the synaptic characteristics of our devices achieved a high recognition accuracy of 91.77% in learning and inference tests and showed clear digital classification results.

Perspective on Nanofluidic Memristors: from Mechanism to Application

Nanofluidic memristors are memory resistors based on nanoconfined fluidic systems exhibiting history-dependent ion conductivity. Toward establishing powerful computing systems beyond the Harvard architecture, these ion-based neuromorphic devices attracted enormous research attention owing to the unique characteristics of ion-based conductors. However, the design of nanofluidic memristor is still at a primary state and a systematic guidance on the rational design of nanofluidic system is desperately required for the development of nanofluidic-based neuromorphic devices. Herein, we proposed a systematic review on the history, main mechanism and potential application of nanofluidic memristors in order to give a prospective view on the design principle of memristors based on nanofluidic systems. Furthermore, based on the present status of these devices, some fundamental challenges for this promising area were further discussed to show the possible application of these ion-based devices.

Controllable Shrinking Fabrication of Solid-State Nanopores

Nanopores have attracted widespread attention in DNA sequencing and protein or biomarker detection, owning to the single-molecule-scale detection accuracy. Despite the most use of naturally biological nanopores before, solid-state nanopores are widely developed with strong robustness, controllable sizes and geometries, a wide range of materials available, as well as flexible manufacturing. Therefore, various techniques typically based on focused ion beam or electron beam have been explored to drill nanopores directly on free-standing nanofilms. To further reduce and sculpt the pore size and shape for nano or sub-nano space-time sensing precision, various controllable shrinking technologies have been employed. Correspondingly, high-energy-beam-induced contraction with direct visual feedback represents the most widely used. The ability to change the pore diameter was attributed to surface tension induced original material migration into the nanopore center or new material deposition on the nanopore surface. This paper reviews typical solid-state nanopore shrinkage technologies, based on the careful summary of their principles and characteristics in particularly size and morphology changes. Furthermore, the advantages and disadvantages of different methods have also been compared completely. Finally, this review concludes with an optimistic outlook on the future of solid-state nanopores.

Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing

By exploiting ion transport phenomena in a soft and flexible discrete channel, liquid material conductance can be controlled by using an electrical input signal, which results in analog neuromorphic behavior. This paper proposes an ionic liquid (IL) multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL₄ and H₂O into the two ends of a discrete polydimethylsiloxane (PDMS) channel. The spike rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP) behavior are highly stable by modulating the input signal. Furthermore, the discrete channel device presents highly durable performance under mechanical bending and stretching. Using the obtained parameters from the proposed ionic liquid-based synaptic device, convolutional neural network simulation runs to an image recognition task, reaching an accuracy of 84%. The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.

Adhesive lift method for patterning arbitrary-shaped thin ion-selective films in micro/nanofluidic device

Micro/nanofluidic platforms with nanoporous films have been utilized as research tools for studying electrokinetic phenomena occurring not only in macro-scale systems such as electro-desalination but also in micro-scale systems such as bio-molecular preconcentrators. However, due to the limitations of fabrication techniques, studies with nanoporous films are mainly limited to vary the physicochemical properties of the films such as surface charge and pore size, despite the enormous effect of the membrane morphology on the phenomena that is to be expected. Therefore, we propose an economic and feasible nanofabrication method called the "adhesive lift method" for patterning thin arbitrarily-shaped nanoporous film to integrate it into micro/nanofluidic platforms. The conformal patterning of the nanoporous films (Nafion or poly(3,4-ethylenedioxythiophene)polystyrene sulfonate (PEDOT:PSS) in this work) was accomplished with spin coating, oxygen plasma treatment and the "adhesive lift technique". Using the fabricated platforms, the initiation of ion concentration polarization along the film with various shapes was demonstrated. In particular, various electrokinetic characteristics of overlimiting conductance depending on the length scale of the microchannels were successfully demonstrated. Therefore, the presented adhesive lift method would provide platforms which can nearly mimic practical macro-scale fluidic systems so that the method would be very useful for studying various electrokinetic phenomena inside it.

Comparison Study on Single Nucleotide Transport Phenomena in Carbon Nanotubes

To be considered as a promising candidate for mimicking biological nanochannels, carbon nanotubes (CNTs) have been used to explore the mass transport phenomena in recent years. In this study, the single nucleotide transport phenomena are comparatively studied using individual CNTs with a length of ∼15 μm and diameters ranging from 1.5 to 2.5 nm. In the case of CNTs with a diameter of 1.57-1.98 nm, the current traces of nucleotide transport are independent with the metallicity of CNTs and consist of single peak current pulses, whereas extraordinary stepwise current signals are observed in CNT with a diameter of 2.33 nm. It suggests that there is only one molecule in the nanochannel at a time until the diameter of CNT increases to 2.33 nm. Furthermore, it also demonstrates that the single nucleotides can be identified statistically according to their current pulses, indicating the potential application of CNT-based sensors for nucleotides identification.

Rational ion transport management mediated through membrane structures

Unique membrane structures endow membranes with controlled ion transport properties in both biological and artificial systems, and they have shown broad application prospects from industrial production to biological interfaces. Herein, current advances in nanochannel-structured membranes for manipulating ion transport are reviewed from the perspective of membrane structures. First, the controllability of ion transport through ion selectivity, ion gating, ion rectification, and ion storage is introduced. Second, nanochannel-structured membranes are highlighted according to the nanochannel dimensions, including single-dimensional nanochannels (i.e., 1D, 2D, and 3D) functioning by the controllable geometrical parameters of 1D nanochannels, the adjustable interlayer spacing of 2D nanochannels, and the interconnected ion diffusion pathways of 3D nanochannels, and mixed-dimensional nanochannels (i.e., 1D/1D, 1D/2D, 1D/3D, 2D/2D, 2D/3D, and 3D/3D) tuned through asymmetric factors (e.g., components, geometric parameters, and interface properties). Then, ultrathin membranes with short ion transport distances and sandwich-like membranes with more delicate nanochannels and combination structures are reviewed, and stimulus-responsive nanochannels are discussed. Construction methods for nanochannel-structured membranes are briefly introduced, and a variety of applications of these membranes are summarized. Finally, future perspectives to developing nanochannel-structured membranes with unique structures (e.g., combinations of external macro/micro/nanostructures and the internal nanochannel arrangement) for mediating ion transport are presented.

Infochemistry and the Future of Chemical Information Processing

Nowadays, information processing is based on semiconductor (e.g., silicon) devices. Unfortunately, the performance of such devices has natural limitations owing to the physics of semiconductors. Therefore, the problem of finding new strategies for storing and processing an ever-increasing amount of diverse data is very urgent. To solve this problem, scientists have found inspiration in nature, because living organisms have developed uniquely productive and efficient mechanisms for processing and storing information. We address several biological aspects of information and artificial models mimicking corresponding bioprocesses. For instance, we review the formation of synchronization patterns and the emergence of order out of chaos in model chemical systems. We also consider molecular logic and ion fluxes as information carriers. Finally, we consider recent progress in infochemistry, a new direction at the interface of chemistry, biology, and computer science, considering unconventional methods of information processing.

Synaptic Iontronic Devices for Brain-Mimicking Functions: Fundamentals and Applications

Inspired by the information transmission mechanism in the central nervous systems of life, synapse-mimicking devices have been designed and fabricated for the purpose of breaking the bottleneck of von Neumann architecture and realizing the construction of effective hardware-based artificial intelligence. In this case, synaptic iontronic devices, dealing with current information with ions instead of electrons, have attracted enormous scientific interests owing to their unique characteristics provided by ions, such as the designability of charge carriers and the diversity of chemical regulation. Herein, the basic conception, working mechanism, performance metrics, and advanced applications of synaptic iontronic devices based on three-terminal transistors and two-terminal memristors are systematically reviewed and comprehensively discussed. This Review provides a prospect on how to realize artificial synaptic functions based on the regulation of ions and raises a series of further challenges unsolved in this area.

Soft and flexible: core-shell ionic liquid resistive memory for electronic synapses

The human brain is the most efficient computational and intelligent system, and researchers are trying to mimic the human brain using solid-state materials. However, the use of solid-state materials has a limitation due to the movement of neurotransmitters. Hence, soft memory devices are receiving tremendous attention for smooth neurotransmission due to the ion concentration polarization mechanism. This paper proposes a core-shell soft ionic liquid (IL)-resistive memory device for electronic synapses using Cu/Ag@AgCl/Cu with multistate resistive behavior. The presence of the Ag@AgCl core shell in the liquid electrolyte significantly helps to control the movement of Cu²⁺ ions, which results in multistate resistive switching behavior. The core-shell IL soft memory device can open a gateway for electronic synapses.

Quasi-Stable Salt Gradient and Resistive Switching in Solid-State Nanopores

Understanding and control of ion transport in a fluidic channel is of crucial importance for iontronics. The present study reports on quasi-stable ionic current characteristics in a SiN_x nanopore under a salinity gradient. An intriguing interplay between electro-osmotic flow and local ion density distributions in a solid-state pore is found to induce highly asymmetric ion transport to negative differential resistance behavior under a 100-fold difference in the cross-membrane salt concentrations. Meanwhile, a subtle change in the salinity gradient profile led to observations of resistive switching. This peculiar characteristic was suggested to stem from quasi-stable local ion density around the channel that can be switched between two distinct states via the electro-osmotic flow under voltage control. The present findings may be useful for neuromorphic devices based on micro- and nanofluidic channels.

Electroosmosis-Driven Nanofluidic Diodes

Fundamental understanding of ion transport in a fluidic channel is of critical importance for realizing iontronics. Here we report on asymmetric ion transport in a low thickness-to-diameter aspect ratio nanopore. Under uniform salt concentration conditions, the cross-pore ionic current showed ohmic characteristics with no bias polarity dependence. In stark contrast, despite the weak ion selectivity expected for the relatively large nanopores employed, we observed diode-like behavior when a salt gradient was imposed across the thin membrane. This unexpected result was attributed to the electroosmotic flow that served to modulate the access resistance through dragging the condensed ions into or out of the nanopore orifices. The simple mechanism was also revealed to be effective in fluidic channels of various size from micro- to nanoscale enabling rectification of the property engineering by the pore geometries. The present findings allow for novel designs of artificial ion channel building blocks.

The Design of 3D-Interface Architecture in an Ultralow-Power, Electrospun Single-Fiber Synaptic Transistor for Neuromorphic Computing

Synaptic electronics is a new technology for developing functional electronic devices that can mimic the structure and functions of biological counterparts. It has broad application prospects in wearable computing chips, human-machine interfaces, and neuron prostheses. These types of applications require synaptic devices with ultralow energy consumption as the effective energy supply for wearable electronics, which is still very difficult. Here, artificial synapse emulation is demonstrated by solid-ion gated organic field-effect transistors (OFETs) with a 3D-interface conducting channel for ultralow-power synaptic simulation. The basic features of the artificial synapse, excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and high-pass filtering, are successfully realized. Furthermore, the single-fiber based artificial synapse can be operated by an ultralow presynaptic spike down to -0.5 mV with an ultralow reading voltage at -0.1 mV due to the large contact surface between the ionic electrolyte and fiber-like semiconducting channel. Therefore, the ultralow energy consumption at one spike of the artificial synapse can be realized as low as ≈3.9 fJ, which provides great potential in a low-power integrated synaptic circuit.

Evolution map of the memristor: from pure capacitive state to resistive switching state

Memristors possess great application prospects in terabit nonvolatile storage devices, memory-in-logic algorithmic chips and bio-inspired artificial neural network systems. However, "what is the origin state of the memristor?" has remained an unanswered question for half a century. While many applications rely on the memristor, its origin state is becoming a fundamental issue. Herein, we reveal a new state, the pure capacitance state (PCS), which occurs before the memristor is triggered, and the origin state of the memristor can be verified in the memory cells through controlling the ambience parameters. Discovery of the PCS, a missing earlier stage of the memristor, completes the whole evolution map of the memristor from the very beginning to the final developed state.