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Review

Chemical Principles in Regulating Nanofluidic Memristors

by
Jiahui Zhou
1,
Haotong Li
2 and
Yaqi Hou
3,*
1
Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
2
Department of Polymer Materials, College of Materials, Xiamen University, Xiamen 361005, China
3
The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005, China
*
Author to whom correspondence should be addressed.
Chemistry 2025, 7(4), 133; https://doi.org/10.3390/chemistry7040133
Submission received: 23 June 2025 / Revised: 5 August 2025 / Accepted: 14 August 2025 / Published: 19 August 2025
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Abstract

Nanofluidic memristors are an emerging class of devices that harness ion transport in confined nanoscale environments to achieve tunable resistance states, mimicking biological synaptic functions. The regulation of ion migration, accumulation, and depletion in nanofluidic channels is fundamentally governed by chemical principles, including surface charge modulation, electrostatic interactions, and ion adsorption and desorption processes. This review provides a comprehensive overview of the chemical foundations of nanofluidic memristors, including electric double layer theory, ion transport dynamics, and interfacial chemistry. Additionally, this review further explores how interfacial chemical modifications, such as functionalization with charged species, pH-responsive coatings, and ionic selectivity molecules, influence nanofluidic memristive behaviors. Representative case studies are discussed to illustrate the practical implementation of these principles in applications ranging from neuromorphic computing to biosensing and energy storage. By bridging fundamental chemical theories with real-world applications, this review aims to provide insights into the rational design of next-generation nanofluidic memristive devices.

1. Introduction

Nanofluidic memristors are emerging as promising candidates for ion-based computing and neuromorphic applications, where ionic transport in confined nano spaces leads to tunable conductance states. Unlike traditional solid-state memristors, which depend on electron transport, nanofluidic systems harness ion migration, making them inherently more compatible with biological systems, such as neurons and synapses [1,2,3]. This bio-compatibility opens new possibilities for low-power, adaptive, and biochemically responsive computing platforms, particularly in edge intelligence and brain-inspired systems [4,5].
At the core of these devices lies the principle of chemically regulated ion transport, which governs memristive behaviors. Ion migration, accumulation, and depletion within nanoconfined environments are controlled by several key chemical principles: electric double layer (EDL) dynamics, ion–surface interactions, and electrostatic modulation via surface charge or functional coatings [6,7,8]. These effects are strongly influenced by nanoscale confinement, channel geometry, and external stimuli such as the pH or applied voltage bias [8,9,10].
A distinctive advantage of nanofluidic memristors lies in their highly tunable ionic conductivity, which can be achieved through the chemical modification of interfaces. Strategies include pH-responsive coatings that dynamically regulate surface charge density [10,11,12], polyelectrolyte multilayers for mediate selective ion gating [13,14,15], and bioinspired coatings that emulate natural ion channels for precise ion flux control [15,16,17,18]. These chemical design strategies not only enhance synaptic emulation, such as short- and long-term plasticity, but also improve device adaptability and selectivity across diverse applications [2,19,20].
Recent advances have demonstrated the feasibility of constructing neuromorphic nanofluidic devices capable of mimicking biological synaptic behaviors, such as spike timing-dependent plasticity (STDP), paired-pulse facilitation (PPF), and long-term potentiation (LTP), by exploiting voltage-driven ionic dynamics [4,20,21]. Furthermore, the integration of ion-selective membranes, asymmetric nanochannels, and mechano-ionic actuation allows for the realization of multifunctional devices with concurrent sensing, memory, and learning capabilities [5,22,23].
This review explores the fundamental chemical principles underpinning nanofluidic memristors and their influence on ionic behaviors in confined geometries. It further discusses how surface functionalization and interfacial chemistry can be strategically employed to enhance device performance in practical applications such as neuromorphic computing, biosensing, and energy information-coupled systems (Figure 1). By bridging molecular-level chemical insights with macroscopic device behavior, this review aims to provide insights to guide the development of chemically tunable nanofluidic memristors.

2. Basic Concepts of Memristors

Memristors, recognized as the fourth fundamental passive circuit component alongside resistors, capacitors, and inductors, are unique nonlinear devices whose resistance values depend not only on the instantaneous current or voltage but also on historical variations in internal state variables. This concept was first proposed by Professor Leon Chua in 1971 [24,25]. The resistance (or conductance) of memristors changes with variations in their internal state variables, such as ion migration, charge accumulation, or other physicochemical processes. These internal processes endow memristors with a distinctive memory function in circuits, enabling them to store information about past electrical stimuli. A key characteristic of memristive behavior is the presence of pinched hysteresis loops in the current–voltage (I-V) curves under periodic voltage sweeps, reflecting their nonlinear and history-dependent conduction characteristics [26,27,28].
Professor Chua theoretically predicted the existence of memristors and derived differential equations describing flux and charge dynamics. The operation of memristors is based on dynamic modulation of internal state variables; when current flows through a memristor, internal ions or charges are redistributed, thereby altering its resistance. These resistance changes are reversible, returning to their initial state once the current is reversed or removed. This property makes memristors potentially valuable for simulating biological neuron electrical activities [29]. In 2008, HP Labs researchers experimentally demonstrated the first nanoscale devices exhibiting memristive behavior [25], providing empirical validation of Chua’s theoretical model and unlocking new avenues for the practical deployment of memristors in advanced computing architectures.
Memristors demonstrate broad application prospects in non-volatile storage, neuromorphic computing, and biomedical engineering. In the biomedical field [30,31], they have been employed to simulate the electrical activities of biological neurons, particularly the dynamic behavior of ion channels. Studies have shown that potassium and sodium ion channels in biological neurons can be regarded as memristors, characterized by hysteresis loops in their I-V responses. This insight reveals that the memory behavior of biological ion channels originates from the coupling of ion transport and protein conformational dynamics within nanoconfined spaces. Such findings offer a biophysical framework for the rational design of bioinspired nanofluidic memristors.
Building on this theoretical foundation, the concept of nanofluidic memristors emerged. A nanofluidic memristor is a nanofluidic device where resistance states are dynamically regulated by historical voltage and ion flow through ion concentration polarization or interface charge modulation within artificial nanopores [32]. The essence of biomimetic design lies in replicating the memory mechanisms of biological ion channels. For instance, conical glass nanopores (1–100 nm in diameter) exhibit voltage-dependent ion enrichment (low-resistance state) or depletion (high-resistance state) due to geometric asymmetry, with their I-V curves exhibiting hysteresis loops similar to biological channels, directly simulating potassium channel memory dynamics [33].
These bioinspired nanofluidic memristors utilize ions rather than electrons or holes as signal carriers, which distinguishes them from solid-state memristors. This fundamental difference imparts several unique advantages. First, the absence of electron-hole recombination ensures greater stability in dynamic processes. Second, the chemical diversity of ionic species provides enhanced potential for information encoding and parallel signal processing, offering greater design flexibility. Third, ions carrying the same charge possess masses several orders of magnitude greater than those of electrons (>1000×), making them inherently more resistant to external noise. Importantly, bioinspired nanofluidic iontronics emulate both the signal transmission mechanisms and aqueous environment of the human brain using the same types of ionic carriers. This intrinsic compatibility significantly enhances the potential for direct interfacing between artificial nanofluidic devices and biological neural systems, paving the way for brain–computer interfaces.

3. Fundamental Chemical Concepts in Nanofluidic Memristors

3.1. Electric Double Layer (EDL) Theory

The electric double layer (EDL) is a fundamental electrochemistry concept that plays a pivotal role in nanofluidic memristors. The EDL crucially influences ionic conductivity, thereby enabling memristive behavior that mimics the synaptic functions in biological systems.

3.1.1. Structure of the EDL

The EDL forms at the interface between a solid surface, such as the wall of a nanofluidic channel, and an electrolyte solution, playing a critical role in regulating ionic transport within confined environments. It consists of two layers, namely the stern layer, where counterions are directly adsorbed onto the charged surface of the nanofluidic channel to neutralize surface charges, and the diffusion layer, where solvated ions are loosely associated with the surface and remain mobile in the solution [34]. These mobile ions significantly contribute to the overall ionic conductance of the system, directly influencing the memristive behavior of nanofluidic devices.

3.1.2. Influence of EDL on Memristive Behaviors

The dynamics of ion transport within the EDL are crucial for the function of nanofluidic memristors. Under an applied electric field, ion migration within the EDL alters the local electric potential, modulating the resistance states of the memristor. Taking the conical nanochannel as an example, the presence of an EDL in the conical nanochannel has two effects. Firstly, it brings about the ion selectivity of the nanochannel such that for nanochannels with positively charged surfaces, only anions can enter and transport through the nanochannel. Secondly, due to the asymmetric geometry of the nanochannels, this allows the EDL to overlap differently in different regions of the nanochannels, which further allows for the ion concentration at the tip and bottom of the nanochannel to be different, with a high ion concentration at the tip and a low ion concentration at the bottom. As shown in Figure 2a, when a positive voltage bias is applied to the bottom of the nanochannel, the anions in the nanochannel move from the tip, where the ion concentration is high, to the bottom, where the ion concentration is low, and at this time, the number of ions entering the nanochannel is greater than the number of ions exiting the nanochannel, while the ions inside the nanochannel are enriched to show a high-conductance state. When a positive voltage bias is applied to the tip of the nanochannel (Figure 2b), the anions in the nanochannel move from the bottom end to the tip end, at which time the number of ions flowing out of the nanochannel is larger than the number of ions entering the nanochannel, and the ions inside the nanochannel are depleted, presenting a low-conductance state. This brings about a change in the ionic conductance of the nanochannel and produces a memristive effect.
Furthermore, surface charge modulation of the nanofluidic channels influences the EDL structure and, consequently, the ion distribution. By modulating this surface charge, one can precisely tune the ion transport properties and the memristive response [6,8]. For example, the use of pH-responsive coatings and functionalized surfaces can modify the EDL and enhance the control over ion migration, thereby allowing for the precise tuning of resistance states. This property is an essential feature for mimicking the adaptive learning processes of biological synapses [35,36], which is critical for applications in neuromorphic computing. Electrostatic interactions between the charged channel surface and ionic species in the EDL are also critical and can be modulated by external stimuli, such as an applied voltage bias. These interactions can alter the EDL’s properties and impact the memristive characteristics of the devices. A deep understanding and precise control of the EDL enable the development of more efficient and responsive memristors. Continued efforts in EDL modeling and interface engineering are critical for advancing the design and application of nanofluidic memristors in brain-like computing.

3.2. Ion Diffusion, Electrophoresis, and Electroosmosis

Ion transport in nanofluidic channels is also governed by a combination of diffusion, electrophoresis, and electroosmosis. Diffusion, described by Fick’s laws, involves the ions spreading from regions of a high concentration to regions of a low concentration due to random thermal motion [37]. In confined nanofluidic environments, diffusion helps equilibrate concentration gradients. In the absence of external forces, diffusion balances the ionic distribution within the channel, maintaining a baseline level of ionic conductance [38,39]. The diffusion rate is influenced by both the concentration gradient and channel geometry. In nanofluidic memristors, diffusion can counteract the effects of electric fields, smoothing out potential fluctuations and contributing to the hysteresis loop observed in I-V characteristics.
Electrophoresis refers to the movement of charged ions toward an electrode of the opposite charge under an applied electric field [40]. The electrophoretic mobility is a function of the ion’s charge and the medium’s viscosity [41]. Electrophoresis is the dominant mechanism for ion transport when an external electric field is applied. It drives the directional movement of ions, which is critical for the switching behavior of memristors. The electrophoretic movement of ions contributes to ion enrichment and depletion within the nanofluidic device, thereby modulating resistance states for information storage and processing.
Electroosmosis refers to movement of the electrolyte relative to a stationary charged surface under an electric field, arising from interactions between the electric field and the EDL formed at the channel walls [39,41,42]. Electroosmotic flow can either enhance or impede ion transport, depending on the alignment of the flow with the desired ion movement. In nanofluidic systems, where surface effects dominate, electroosmosis can significantly impact device behavior either by accelerating or decelerating ion enrichment and depletion [43], affecting the speed and stability of memristive switching. By tuning electroosmotic conditions, one can optimize the hysteresis characteristics of the device for specific applications.
The interplay of diffusion, electrophoresis, and electroosmosis governs the rate of ion accumulation and depletion within nanofluidic channels [44], which directly influences the hysteresis behavior characteristic of memristors, typically manifested as hysteresis loops with varying areas. This hysteresis loop, reflecting the history-dependent resistance change, is a hallmark of memristive functionality. Factors such as the ionic strength, channel geometry, and external bias conditions must be carefully controlled to achieve desirable memristive effects. Elevated ionic strength can enhance ion mobility and modulate the EDL, while channel geometry influences the relative contributions of each transport mechanism. By systematically understanding and manipulating these factors, researchers can design nanofluidic memristors with tailored properties for applications in neuromorphic computing, adaptive learning, and beyond.

3.3. Physicochemical Mechanisms Underlying Memristive Effects in Nanofluidics

The memristive effects in nanofluidic devices primarily stem from transient polarization of ion concentration within confined spaces, which dominates device resistance at memory timescales. Upon the application or reversal of an external bias, changes in both the amplitude and spatial distribution of ions within the channel arise from three main mechanisms: asymmetric design [45,46,47,48,49,50,51], state switching during transport [52,53,54,55], and mechanical deformation-induced channel volume changes [5].
Asymmetric design serves as a critical method for regulating the ion concentration distribution, including geometric, charge, concentration, and compositional asymmetric design. In geometric asymmetric channels (Figure 3a) [45], parameters such as the migration rate and diffusion coefficient of ions vary depending on the location. This makes the ion transport kinetics at different positions different under the same applied voltage, which leads to uneven distribution of ion concentrations throughout the channel, forming a memristive effect. For example, in conical nanopores, tip dimensions often approach the EDL thickness. The confined geometry leads to ion-selective transport via steric hindrance and directional enrichment or depletion, generating conductance hysteresis under voltage scans. Additionally, geometric asymmetry can also lead to uneven distribution of electric fields within the channel. In the tip or narrow region, the electric field strength is relatively high, which accelerates the migration and enrichment of ions in these areas. At the same time, the high electric field region is also more likely to trigger interactions between ions and channel walls, such as adsorption or desorption, which further changes the local ion concentration and affects the ionic conductance of the channel.
In charge asymmetric devices (Figure 3b) [46], the surface charge density or polarity of the inner wall of the channel is unevenly distributed along the length of the channel. This non-uniform surface charge distribution creates electrostatic potential gradients that selectively attract or repel ions. This results in spatially dependent ion enrichment and depletion along the channel length, leading to asymmetric ionic current responses. At the same time, local mutations in the surface charge distribution can significantly alter the fluid transport characteristics [47,48]. For example, a sudden change in charge can alter the migration path and velocity of ions, leading to the enrichment or depletion of ions in specific regions and resulting in memristive effects.
The concentration’s asymmetric (Figure 3c) [21,36,49] mechanism forms a concentration gradient by applying different concentrations of electrolytes at both ends of the channel, which affects the migration and distribution of ions when superimposed with an applied electric field. When the direction of the applied voltage is consistent with the concentration gradient, ion migration is facilitated, promoting conductance. Conversely, when the voltage direction is opposite to the concentration gradient, ion migration is hindered, reducing conductance. This asymmetry in ion distribution makes the conductance of the device dependent on the scan direction and history of the voltage, resulting in a memristive effect.
The compositional asymmetric (Figure 3d) [50,51] mechanism involves the use of different ionic species (e.g., KCl versus ionic liquids) or valence states at both ends. Differential ion–wall interactions and mobility shift the dominant charge carriers upon voltage reversal, resulting in interface displacement and modulation of the effective resistance.
Transmission state switching refers to the reversible regulation of device conductance through transitions in physical or chemical states within nanoconfined spaces. Typical mechanisms include ion adsorption and desorption, polyelectrolyte-ion pairs, and wettability transitions at channel interfaces.
The adsorption- and desorption-based memristive mechanism (Figure 3e) [52] is a nanofluidic device based on the dynamic adsorption and desorption process of ions in nanochannels for modulating conductance. Polyelectrolyte-ion pair transition memristors rely on the dynamic equilibrium of polyelectrolyte and ion pairs in confined spaces to operate (Figure 3f) [53,54]. Hydrophilicity-switching memristors (Figure 3g) [55] rely on voltage-induced wettability transitions at the nanopore interface. Application of an electric field induces water molecule alignment into a conductive “waterline” along the hydrophobic channel surface, enabling ion conduction in a low-resistance state. Upon removal of the voltage, the surface reverts to its hydrophobic state, obstructing ion flow and restoring high resistance. This mechanism enables the device to have switch-state memory functions, enabling precise regulation of ion transport.
Some nanofluidic memristors achieve memory behavior through mechanical deformation driven by the voltage. The voltage-driven mechanical deformation mechanism (Figure 3h) [5] induces reversible deformation of the flexible channel material through an applied voltage, changing the effective cross-sectional area and volume of the channel and thereby regulating the ion transport. Under forward bias, the flexible material expands, the channel widens, and the ion transport resistance decreases, forming a low-resistance state. Conversely, reverse bias induces the flexible material shrinks, the channel narrows, and the ion transport is blocked, forming a high-resistance state. This deformation has a memory effect, and even after the voltage is removed, the resistive state of the channel is maintained until a subsequent voltage is applied again.

4. Chemical Design Strategies of Nanofluidic Memristors

Nanofluidic memristors modulate ion transport through nanoscale channels, enabling the simulation of synaptic functions. The chemical composition of the nanoconfinement surface significantly affects ion transport. Surface functionalization with charged or responsive molecules or specific responsive coatings can introduce ionic selectivity, allowing for enhanced control over ion enrichment and depletion. One critical design principle is the ability to control the transport properties of ions according to different environmental conditions (e.g., pH or external electric field), thus mimicking the selectivity and adaptability of ion pumps in biological nervous systems.

4.1. pH-Responsive Coatings

The regulation of ion migration is critically influenced by chemical modifications, specifically pH-responsive coatings that alter the charge density in response to environmental changes. The operational mechanism of pH-responsive nanofluidic devices involves dynamic modulation of the permeability, selectivity, and surface charge in response to environmental pH changes. This functionality is enabled by integrating charged molecules or polymers into nanochannels, which respond dynamically to environmental pH variations [11,12].
For example, studies demonstrated that with pH-responsive coatings, pH variations allow for the reversible tuning of conductance states (Figure 4a) [10]. The asymmetrical distribution of pH on both sides of the nanochannel led to reversal of the direction of the hysteresis loop. When the surface carboxylic acid groups showed different pH-dependent ionization states, in the negatively charged pores, when the voltage was >0, the solution cations accumulated at the cone tip, resulting in high pore conductance. At V < 0, these ions were depleted at the tip of the cone, resulting in low pore conductivity. For positively charged holes, the opposite memristive effect was observed. Therefore, the neuromorphic-like potentiation of conductance can be induced by voltage pulses (Figure 4b). By applying different pH configurations to the external solution of the nanochannels, the conductance will change significantly over time, enabling the pH-modulated conductance to respond to different voltage pulses, which is essential for applications in neuromorphic systems [2].
Additionally, these coatings enhance the selectivity and efficiency of ion transport in biosensing applications, providing pathways for selective ion transport similar to biological ion channels [56]. At a low pH, these coatings may exhibit increased affinity for protons, while at a high pH, selectivity for other ions may be enhanced. This adaptive behavior allows nanofluidic memristors to maintain a high degree of design flexibility under various environmental conditions [10,57]. Researchers designed a pH-responsive nanofluidic memristor, which was able to regulate ion flow in response to changes in external pH conditions, thus mimicking neural synapses. This suggests that environmental modulation of ionic transport behavior can effectively regulate device performance [58] and is critical for designing neuromorphic computing devices [19].

4.2. Polyelectrolyte Layers

Polyelectrolyte layers play a pivotal role in modulating electrostatic interactions and facilitate selective ion gating in nanofluidic systems, thereby enhancing memristive behavior [13,14]. The introduction of polyelectrolyte layers enhances the memristive properties of nanofluidic devices by allowing precise control over ion transport and accumulation. For example, researchers described a porous nanofluidic memristor with conical pores, using polyimide foil as the polyelectrolyte substrate. By adjusting the amplitude and frequency of the external voltage, as well as the type of salt and ion concentration, they obtained different memristor effect strengths, which were quantified with the area enclosed by the current I-V curves (Figure 5a) [10,15]. The functional characteristics of the porous membrane for simulating the biological ion channel can be reversed by chemically changing the pH values to achieve the reverse modulation memristive effect (Figure 5b). Furthermore, the interplay between polyelectrolyte layers and pH-responsive coatings involves altering the surface charge and permeability of nanofluidic devices, allowing for responsive and adaptive ion transport that improves device performance across various applications [59,60].

4.3. Biomimetic Coatings

The integration of biomimetic coatings, inspired by biological ion channels, enhances selective ion transport pathways, which are vital for applications in neuromorphic computing, biosensing, and energy storage.
Biomimetic coatings replicate the structural and functional characteristics of natural ion channels, providing selective ion transport pathways and enabling efficient and selective ion gating in nanofluidic devices [10,15]. Firstly, bioinspired nanostructures (e.g., symmetrical and asymmetrical channels) are widely used in the design of nanofluidic memristors. These structures mimic natural ion channels (e.g., aquaporins and sodium-potassium pumps) to enable efficient and selective ion transport at the nanoscale [61,62]. The geometrical design of asymmetric channels not only generates an electric field gradient that varies the transport speed and selectivity of ions within the channel but also accelerates the migration of ions and regulates the accumulation of ions in different regions of the channel to modulate the ionic conductance [23,63,64,65]. With this design, various conductance states can be quickly and reversibly adjusted within the nanofluidic memristors.
Furthermore, by incorporating charged molecules and polymers, biomimetic coatings improve ion transport dynamics and contribute to the adaptive functionality of nanofluidic devices [2,16]. For instance, a study demonstrated that strong polyelectrolyte brushes (SPBs) exhibit pH-responsive behavior due to the reorganization of interchain hydrogen bonds triggered by the pH-mediated adsorption-desorption equilibrium of hydronium or hydroxide ions. This molecular-level mechanism allows SPBs to dynamically adjust their conformation, hydration, and stiffness, thereby modulating ion transport properties, similar to biological ion channels [17]. Nanochannels with biomimetic coatings have the ability to selectively transport ions [18], which makes them particularly valuable for mimicking neural synapses and achieving high specificity in biosensing applications.

5. Practical Implementation of Chemical Design in Nanofluidic Memristors

5.1. Ionic Synapse Mimicry

Synaptic plasticity refers to the ability of neural synapses to modulate signal transmission over time, serving as the basis for learning and memory in biological neural networks. It supports information storage and adaptive responses to external stimuli by adjusting the connection strength (synaptic weights). Synaptic plasticity can be categorized into excitatory synapses and inhibitory synapses, based on the different response patterns of the synapses [66,67]. In neuromorphic computing, emulating synaptic plasticity enables the development of computational systems that more closely emulate biological processes, allowing for learning, environmental adaptation, and performance optimization (Figure 6a) [68]. Nanofluidic memristors enable the controlled flow of ions by regulating the concentration and electric field of ions in nanoscale fluidic channels. The enrichment and depletion of these ions directly affect the conductivity of the channel, thus mimicking the phenomena of synaptic excitation and inhibition. This capability of nanofluidic memristors offers a novel hardware foundation for neuromorphic computing [21]. For example, in paired-pulse facilitation (PPF) experiments, researchers mimic synaptic excitation in biological synapses by adjusting the ion concentration gradient so that two consecutive voltage pulses result in increased conductance during the second pulse. Conversely, paired-pulse depression (PPD) was also effectively reproduced in the ion concentration gradient nanofluidic memristors (ICGNMs). The application of two consecutive voltage pulses leads to a subsequent decline in ionic conductance over time, mimicking the inhibitory behavior observed in biological synapses (Figure 6b) [69].
Recent studies have demonstrated various implementations of nanofluidic memristors capable of mimicking synaptic behaviors such as PPF, PPD, and spike timing-dependent plasticity (STDP). For instance, researchers developed a polyelectrolyte-confined fluidic memristor (PFM) that successfully replicated short-term plasticity (STP) behaviors, with tunable memory retention dependent on the pulse parameters (Figure 6c) [20]. Similarly, researchers reported a resistance-restorable nanofluidic memristor system based on voltage-driven ion depletion and refilling, achieving conductance recovery over 20,000 cycles and stable neuromorphic performance [4]. Furthermore, researchers designed a mechano-ionic nanofluidic logic device that combined mechanical actuation with ionic flow regulation, enabling reconfigurable synaptic functionality within a single nanopore [5].
Furthermore, a theoretical model was proposed to describe the relaxation dynamics and rectification behaviors in nanofluidic channels, which closely correlate with the frequency-dependent response observed in biological synapses [70]. Other researchers also explored synapse-like long-term memory in 2D nanofluidic channels and presented a nanofluidic ionic memristor based on confined polyelectrolyte–ion interactions, which shows promising directions for creating neuromorphic functions using energy-efficient fluidic memristors [52]. These studies highlight the potential of nanofluidic memristors to reproduce multiple forms of synaptic plasticity, providing a promising hardware basis for neuromorphic computation and brain-inspired information processing.

5.2. Integration into Next-Generation Computing Systems

With the development of artificial intelligence, big data, and edge computing, there is a growing demand for low-power, high-density computing systems with self-learning capabilities. The energy consumption and latency limitations of conventional von Neumann architectures, stemming from the bottleneck of separated memory and processing units, have driven the exploration for new computing architectures. Neuromorphic computing, which simulates the structure and processing mechanisms of the biological brain, is widely regarded as a promising direction for the next generation of intelligent computing. Nanofluidic memristors have emerged as compelling hardware candidates for the implementation of neuromorphic systems due to their features, such as simulating synaptic plasticity, enabling programmable ion transport, and extremely high energy efficiency [71,72,73,74].
By controlling the migration and accumulation of ions to achieve adjustable conductance states, nanofluidic memristors can simultaneously perform computing and storage functions at the device level, thereby overcoming the physical separation of the computing unit and storage unit in conventional computing architectures [75]. This in-memory computing capability is particularly suitable for highly parallel, fault- and noise-tolerant data processing tasks such as image recognition, semantic understanding, and timing prediction. In recent years, researchers have applied these devices to brain-inspired chip prototypes to emulate key synaptic behaviors, including short- and long-term plasticity (STP and LTP, respectively) and Hebbian learning mechanisms [5,76], offering a hardware basis for brain-like computing and energy-efficient adaptive learning systems [4,68,69].
Several research groups have developed nanofluidic memristor arrays based on flexible or printable materials that can be directly connected to conventional electronic components, enabling signal conversion across physical domains. Memristors based on controllable ion migration behavior have been applied to novel ion capacitors or hybrid battery systems, facilitating the integration of energy storage and information processing functions [77,78,79]. Specifically, the “CAPistor” (capacitor + memristor) device integrates a metal-organic framework (MOF)-based nanofluidic channel with ionic conductivity and a conductive substrate to achieve both energy storage and resistive switching functions (Figure 7a) [77]. The device structure typically consists of two electrodes separated by an MOF membrane, where ionic transport and accumulation lead to a capacitive response, while the reversible formation and disruption of ion pathways within the channel induce memristive behavior (Figure 7b,c). This dual-function mechanism enables the CAPistor to simultaneously store energy through electric double-layer capacitance and perform logic operations by modulating ionic conductance, thus serving as a basic unit for neuromorphic energy-informatic systems.

6. Challenges and Future Perspectives

Despite notable advances in the development of nanofluidic memristors, several key challenges remain that hinder their practical implementation. Ion selectivity and long-term stability are major bottlenecks, as device performance often depends on the precise transport of specific ions. However, selectivity is highly sensitive to factors such as the channel geometry, surface charge density, and chemical stability of the functional materials [80]. Additionally, factors such as the surface chemistry, interfacial charge distribution, and ion migration pathways in nanochannels are highly sensitive and prone to fluctuations, causing device performance to drift over extended operation and repeated cycling and compromising device reliability and reproducibility. Addressing these issues requires more precise material design and chemical modulation strategies to enhance ion selectivity and operational stability.
Another key challenge lies in further improving the energy efficiency of nanofluidic memristors for low-power applications. Advances in nanomaterials and surface functionalization are expected to enable more precise control of ion transport, allowing future devices to combine ultra-low power consumption and extended retention with enhanced durability and stability [36].
In addition, to realize the full potential of nanofluidic memristors in next-generation computing systems, several key challenges must be addressed. First, the integration density and device-to-device consistency remain underdeveloped, with the uniform performance of individual elements in large-scale arrays still lacking. Second, the relatively slow ion migration dynamics, compared with electron transport in conventional electronic devices, limit their applications in high-frequency computing scenarios. Furthermore, there is still a lack of systematic understanding of the relationship between ion transport kinetics and long-term device performance. For example, the space charge effect resulting from the enrichment of ions in the channel may lead to irreversible changes, compromising the reversibility and endurance of memristive behavior. Future efforts should focus on multi-scale modeling and experimental validation to uncover the fundamental mechanisms underlying performance degradation [20,81].
Looking ahead, the development of nanofluidic memristors will increasingly rely on the integration of responsive smart materials that can dynamically adjust channel structures and surface properties in response to external stimuli such as electric fields, light, temperature, and chemical changes, enabling adaptive and self-regulating behavior at the device level. Furthermore, closed-loop systems with “sense-regulate-memory” functionality are expected to build self-learning and self-repairing neuromorphic architectures. As both neuron- and synapse-like elements, nanofluidic memristors hold great promise for next-generation computing, with potential applications in edge intelligence, biointerfaces, and brain-like chips [5,82].
To address these challenges, future research should focus on several key directions. First, constructing multifunctional nanocomposite channels by incorporating responsive polymers, functional nanoparticles, or biomimetic materials can enhance adaptability and stability in complex environments while improving ion transport efficiency, uniformity, and dynamic response. Second, developing low-power and highly responsive hybrid ion-electronic driving mechanisms can combine the speed of electronic signals with the plasticity of ionic systems, enabling more efficient and accurate emulation of synaptic behaviors, particularly in high-frequency and real-time processing scenarios. Third, although recent advances in nanofabrication and integration technologies offer promising pathways, significant challenges remain in the integration of nanofluidic memristors.
Resolving these issues requires interdisciplinary efforts and novel engineering strategies to realize robust, multifunctional system-level architectures. Through interdisciplinary innovation spanning materials science, micro- and nanofabrication, neuromorphic engineering, and other multidisciplinary disciplines, nanofluidic memristors are expected to advance from the laboratory to industrial application, becoming core components of next-generation intelligent hardware for smart sensing, brain-like computing, and biointerfaces and providing a foundation for future AI and information technologies.

7. Conclusions

The chemical regulation of ion transport in nanofluidic memristors offers a powerful approach for developing next-generation memory and computing technologies. By understanding and leveraging chemical principles such as EDL modulation, surface functionalization, and ion selectivity, researchers can engineer devices with enhanced stability and tunability. As the field progresses, continued interdisciplinary efforts will be essential in translating these fundamental insights into practical applications.

Author Contributions

J.Z. and H.L. contributed equally to this work. Conceptualization, Y.H.; investigation, J.Z., H.L., and Y.H.; visualization, J.Z. and H.L.; writing—original draft, J.Z. and H.L.; writing—review and editing, J.Z., H.L., and Y.H.; supervision, Y.H.; validation, Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (No. 52303380), Fundamental Research Funds for the Central Universities (No. 20720240041 and 20720252007), and XMU Undergraduate Innovation Training Programs (No. 202510384061).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Nanofluidic memristors, from chemical mechanisms to applications.
Figure 1. Nanofluidic memristors, from chemical mechanisms to applications.
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Figure 2. Schematic illustration of switching of the (a) high and (b) low ionic conductance states of nanochannels, resulting from ion enrichment and depletion induced by an asymmetric EDL.
Figure 2. Schematic illustration of switching of the (a) high and (b) low ionic conductance states of nanochannels, resulting from ion enrichment and depletion induced by an asymmetric EDL.
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Figure 3. Schematic illustration of representative mechanisms in memristive behaviors in nanofluidic systems. First, asymmetric ionic transport, such as (a) geometric asymmetric, (b) charge asymmetric, (c) concentration asymmetric, and (d) compositional asymmetric design. Second, state switching during ion transport, such as (e) adsorption-desorption transport, (f) polymerization-deploymerization processes, and (g) hydrophilic-hydrophobic switching. Third, (h) elastic dynamic channel deformation.
Figure 3. Schematic illustration of representative mechanisms in memristive behaviors in nanofluidic systems. First, asymmetric ionic transport, such as (a) geometric asymmetric, (b) charge asymmetric, (c) concentration asymmetric, and (d) compositional asymmetric design. Second, state switching during ion transport, such as (e) adsorption-desorption transport, (f) polymerization-deploymerization processes, and (g) hydrophilic-hydrophobic switching. Third, (h) elastic dynamic channel deformation.
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Figure 4. Reversible regulation of conductivity states by pH-responsive coatings. (a) The hysteresis loops at different pH levels on both ends of the channels. (b) The variation in conductivity over time when voltage pulses were applied at different pH levels [10].
Figure 4. Reversible regulation of conductivity states by pH-responsive coatings. (a) The hysteresis loops at different pH levels on both ends of the channels. (b) The variation in conductivity over time when voltage pulses were applied at different pH levels [10].
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Figure 5. Hysteresis loops of porous nanofluidic memristors under different experimental conditions. (a) The I-V curves obtained by changing the frequency, amplitude of voltage, ion concentration, and salt type, respectively. (b) The I-V curves obtained at different pH levels [15].
Figure 5. Hysteresis loops of porous nanofluidic memristors under different experimental conditions. (a) The I-V curves obtained by changing the frequency, amplitude of voltage, ion concentration, and salt type, respectively. (b) The I-V curves obtained at different pH levels [15].
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Figure 6. Ionic nanofluidic synapses. (a) Illustration of multifunctional and dynamically changing biological neural synapses [68]. (b) Paired-pulse facilitation (PPF) and paired-pulse depression (PPD) in ICGNMs [69]. (c) By applying a negative voltage pulse train, frequency-dependent conductivity was obtained in the PFMs [20].
Figure 6. Ionic nanofluidic synapses. (a) Illustration of multifunctional and dynamically changing biological neural synapses [68]. (b) Paired-pulse facilitation (PPF) and paired-pulse depression (PPD) in ICGNMs [69]. (c) By applying a negative voltage pulse train, frequency-dependent conductivity was obtained in the PFMs [20].
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Figure 7. The “CAPistor” with capacitive-coupled memristive behavior and its working mechanism. (a) Equivalent circuit model of the CAPistor device. (b) Typical current–voltage (I-V) curve of the CAPistor, demonstrating the capacitive-coupled memristive behavior. (c) Illustrations of ion transport behavior within the ZIF-7 channel under different bias voltages [77].
Figure 7. The “CAPistor” with capacitive-coupled memristive behavior and its working mechanism. (a) Equivalent circuit model of the CAPistor device. (b) Typical current–voltage (I-V) curve of the CAPistor, demonstrating the capacitive-coupled memristive behavior. (c) Illustrations of ion transport behavior within the ZIF-7 channel under different bias voltages [77].
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Zhou, J.; Li, H.; Hou, Y. Chemical Principles in Regulating Nanofluidic Memristors. Chemistry 2025, 7, 133. https://doi.org/10.3390/chemistry7040133

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Zhou J, Li H, Hou Y. Chemical Principles in Regulating Nanofluidic Memristors. Chemistry. 2025; 7(4):133. https://doi.org/10.3390/chemistry7040133

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Zhou, Jiahui, Haotong Li, and Yaqi Hou. 2025. "Chemical Principles in Regulating Nanofluidic Memristors" Chemistry 7, no. 4: 133. https://doi.org/10.3390/chemistry7040133

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Zhou, J., Li, H., & Hou, Y. (2025). Chemical Principles in Regulating Nanofluidic Memristors. Chemistry, 7(4), 133. https://doi.org/10.3390/chemistry7040133

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