The theoretical privilege of mem-elements among candidate security substrates stems from a single constitutive property: their present electrical response cannot be fully determined without knowledge of their past excitation history. This history dependence translates directly into security-relevant characteristics. The partial inaccessibility of internal state variables provides a physical basis for unclonability, which is the property exploited in physical unclonable functions, or PUFs. Sensitivity to initial conditions and intrinsic switching stochasticity support the unpredictability required by true random number generators, or TRNGs. And the high-dimensional chaotic dynamics of multi-element memory circuits enable sustained entropy generation that resists data-driven inference. The review’s comparative table shows how sharply the three families differ: memristors are commercially available in resistive random access memory and phase-change memory, memcapacitors exist only as laboratory demonstrations in ferroelectric and electrolyte-gated structures, and meminductive behavior in a passive two-terminal element was experimentally reported only in 2023.

Memristor-based PUFs have been realized across a striking range of material platforms, including hafnium oxide-based RRAM, phase-change materials, graphene oxide, and laser-induced graphene, demonstrating competitive uniqueness and reliability metrics. The memristor is particularly well suited to this purpose because its resistance state depends not only on the present bias but on the entire excitation history, and its fabrication-induced variability, which originates from stochastic filament formation, interface roughness, and local dopant fluctuations, produces device-specific signatures that are difficult to replicate. Yet the review identifies a structural vulnerability that runs through both classical delay-based and memristive PUFs: their responses can be modelled with high accuracy from relatively modest challenge-response datasets. Standard arbiter PUFs are broken by logistic regression with prediction accuracies exceeding 99 percent using as few as roughly 2,000 challenge-response pairs, and even XOR-augmented variants require only tens of thousands. The susceptibility is structural rather than incidental, because the underlying low-dimensional state space can be tracked and reproduced by an adversary with sufficient data, regardless of the inference technique employed.

Memristive TRNGs harvest entropy from the stochastic nature of resistive switching events, specifically from the probabilistic nucleation and dissolution of conductive filaments under applied bias, or from charge-trapping fluctuations in ferroelectric structures. Two operational regimes have been exploited: switching-time jitter, where the random delay between voltage application and state transition is digitized to extract random bits, and cycle-to-cycle resistance-state variability harvesting, where the spread in programmed conductance values across successive switching events serves as the entropy source. One notable hardware demonstration integrated a hexagonal boron nitride memristor with a commercial microcontroller, passing all NIST statistical tests at low operating voltages. Compared with thermal-noise or ring-oscillator generators, memristive TRNGs offer clear advantages in miniaturization and CMOS compatibility, though two persistent challenges remain: characterizing the entropy source under aging and cycling stress, and the temperature dependence of filament stochasticity, which can introduce statistical bias at operating extremes and requires post-processing correction or adaptive biasing.

Beyond their use as stochastic switching elements, memristors enable autonomous chaotic oscillators without additional nonlinear components, since the hysteretic nonlinearity of the device itself sustains complex attractor dynamics. Memristive variants of the Chua circuit produce chaotic trajectories in a three-dimensional state space defined by terminal voltage, current, and the internal state variable, with positive Lyapunov exponents providing a well-defined measure of the entropy generation rate. From a security standpoint, the critical property is not chaos alone but its combination with the partial inaccessibility of that internal state to external measurement. An adversary monitoring terminal voltage and current observes only a projected shadow of the full state trajectory, while the internal variable evolves in an unobservable dimension. The main limitation, however, is that memristor-only topologies typically sustain a single positive Lyapunov exponent, constraining attractor dimensionality and the adversary’s prediction horizon, which is exactly what motivates the transition to multi-element memory circuits.

The memcapacitor and the meminductor extend the mem-element family to reactive components. A voltage-controlled memcapacitor is formally defined by a capacitance linking the time integral of charge to the flux, the time integral of voltage, so that its constitutive relation involves the second integral of the current and the first integral of the voltage. The meminductor is defined analogously in the domain relating the time integral of flux to charge. Both devices exhibit pinched hysteresis loops in their respective characteristic planes, confirming the memory nature of their response. The critical distinction from the memristor is that both devices combine memory with energy storage, so their internal state can evolve through passive or weakly dissipative dynamics, with complexity arising without the sustained energy expenditure associated with resistive switching. At the model level, state trajectories are not necessarily subject to the thermal relaxation that affects memristive internal variables at zero bias, a property with potential implications for idle-state retention that awaits experimental validation.

The most significant and least explored scenario arises when memcapacitors and meminductors operate jointly, potentially alongside a memristor. The simultaneous presence of electrical and magnetic memory introduces multiple coupled internal state variables operating across distinct energy domains, each contributing at least one independent state variable defined by its constitutive relation, producing a high-dimensional phase space whose accessible dynamics cannot be reproduced by any single mem-element type alone. This coupling prevents monotonic relaxation, increases effective state-space dimensionality, and enables hyperchaotic regimes with two or more positive Lyapunov exponents, substantially increasing the rate of information generation relative to simple chaotic circuits. Three properties of joint operation are particularly interesting from a security standpoint. Dimensional scaling is structural rather than incidental, since each additional mem-element of a distinct type expands the accessible phase space without active components, making complexity intrinsic to the circuit topology rather than to design effort. Extreme multistability, the coexistence of multiple stable oscillatory regimes accessible from different initial conditions, is directly exploitable in PUF design, because manufacturing variability determines which attractor the circuit settles into, producing a device-specific signature whose diversity grows with attractor dimensionality. Most fundamentally, inference resistance in memcapacitor-meminductor circuits has a physical rather than computational basis: because the state variables of reactive mem-elements evolve under energy-conservative dynamics, reconstructing the full system state from terminal measurements requires solving an inverse problem of substantially higher complexity than for purely resistive systems.

The review is careful to draw a clear boundary between demonstrated and speculative results. Chaotic, hyperchaotic, and multistable dynamics of coupled memcapacitor-meminductor circuits have been verified experimentally through emulator implementations, which faithfully reproduce constitutive relations and circuit-level dynamics while differing from real devices in noise spectral density, thermal behavior, and scalability, all of which are directly security-relevant. The security-specific properties, including attractor-based uniqueness, entropy quality, and inference resistance, remain theoretical propositions awaiting experimental validation. Emulator-based validation faces a deeper limitation: emulators cannot reproduce the fabrication-induced variability and noise properties that are precisely the source of security-relevant entropy in real devices. Overcoming this requires dedicated characterization platforms for emerging memcapacitive implementations, specifically targeted at security metrics rather than at neuromorphic or storage applications. No systematic study has yet characterized memcapacitor- or meminductor-based circuits against entropy or randomness standards, nor formalized the conditions under which their internal states become unobservable from external electrical measurements.

Several critical questions collectively define the frontier. No appropriate benchmarking framework exists: current evaluation tools such as NIST SP 800-22 and standard PUF metrics were designed for digital circuits and do not capture properties specific to continuous-state analog devices. New metrics grounded in dynamical systems theory are needed, such as the Kolmogorov-Sinai entropy rate as a measure of analog entropy generation, or mutual information between terminal measurements and internal state variables as a proxy for inference resistance. By Pesin’s identity, the Kolmogorov-Sinai entropy equals the sum of the positive Lyapunov exponents, directly linking entropy generation to the hyperchaotic dynamics characterized above. A practical route toward standardization is to estimate these indicators from measured time series using established embedding and Lyapunov-spectrum algorithms, validate them on emulator testbeds against a ground-truth model, and ultimately incorporate them as extensions of entropy-source validation methodologies such as NIST SP 800-90B. A further gap concerns the formalization of resistance to modelling attacks: the expanded state-space dimensionality of reactive mem-element circuits is qualitatively argued to improve resilience against machine-learning-based adversaries, but no formal bound on the sample complexity required for successful modelling attacks has been established, and deriving such bounds analogous to Vapnik-Chervonenkis-dimension arguments would transform a qualitative intuition into a verifiable security claim.

The asymmetry documented in the review reflects less a judgment about relative security value than the historical accident of technological maturation: memristors achieved experimental demonstration decades before their reactive counterparts, and security researchers naturally followed the available technology. Established approaches such as SRAM-PUFs, ring-oscillator PUFs, and quantum random number generators each offer well-characterized entropy sources, but none combines history dependence, energy-storing memory, and intrinsic high-dimensional chaos within a single passive circuit element. The unique advantage of memcapacitor-meminductor systems lies precisely in this confluence, where complexity, partial non-observability, and device-specific variability emerge together from the same physical mechanism without requiring digital post-processing or external noise injection. The mathematical framework to exploit this is already in place, combining the constitutive formalism of mem-elements with standard tools of nonlinear dynamical systems theory, including state-space modelling, Lyapunov analysis, and nonlinear observability; what is missing is its systematic application to security-oriented circuit design. The authors outline a plausible research roadmap spanning three horizons: in the short term, circuit-theoretic analysis of observability limits and emulator-based evaluation against established randomness standards; in the medium term, device-level validation of emulator predictions on emerging solid-state memcapacitors; and in the longer term, the deliberate engineering of the high-dimensional state space of multi-mem-element circuits into deployable security primitives, an opportunity that is, at present, entirely unrealized. Four practical challenges will ultimately determine deployability: device-to-device variability, which is double-edged in providing the entropy that sustains uniqueness while threatening response reproducibility; long-term stability and aging, whose failure modes under cycling and thermal stress remain largely uncharacterized for memcapacitors and meminductors; and CMOS integration, where no mem-element material family is yet process-compatible with the others or with standard flows. These device-level problems are expected to intensify as system complexity grows, since each added mem-element multiplies the sources of variability and drift, and in a coupled nonlinear circuit the aging of a single device can reshape the attractor landscape or cross a bifurcation boundary, meaning that reliability engineering must scale with the very dimensionality that provides the security benefits.

Subject of Research: Memory circuit elements, including memristors, memcapacitors, and meminductors, as physical substrates for hardware security primitives such as PUFs and TRNGs.

Article Title: Memory circuit elements for hardware security: a review from memristors to memcapacitors and meminductors

Article References: Romero, F. J., Habboush, S., Hatefinasab, S., Fernandez, C., Ohata, A., Castillo, E., Morales, D. P., & Rodriguez, N. (2026). Memory circuit elements for hardware security: a review from memristors to memcapacitors and meminductors. Frontiers in Electronics, 7, Article 1919384. https://doi.org/10.3389/felec.2026.1919384

Image Credits: AI Generated

DOI: 10.3389/felec.2026.1919384

Keywords: memristor, memcapacitor, meminductor, hardware security, physical unclonable function, true random number generator, entropy generation, nonlinear dynamics, Memory, circuit, elements, hardware

The device itself is deceptively simple in architecture. It consists of a vertical stack of silver, hexagonal boron nitride, and indium tin oxide, all deposited on a flexible polyethylene terephthalate substrate. Hexagonal boron nitride, often nicknamed white graphene, is a layered insulating crystal whose atomically flat sheets can be stacked like a deck of cards. In a conventional memristor, electrical pulses program the resistance state, and mechanical deformation is usually treated as a nuisance, a source of noise or failure to be engineered away. The Shiv Nadar team inverted that logic. In their device, bending is the programming signal. When the flexible substrate flexes, the hBN flakes within the stack slide laterally relative to one another, and that sliding perturbs the stacking arrangement and the inter-flake tunneling distance that governs how easily electrons can hop across the layered insulator.

The elegance of the mechanism lies in what does not happen. Because the programming arises from interlayer sliding rather than from stretching the crystal lattice itself, the hBN sheets are never strained at the atomic level. The covalent bonds within each flake remain intact, which means the process is non-destructive and, crucially, reversible. Straighten the device and the flakes can slide back, restoring the previous tunneling gap and the previous conductance state. This stands in sharp contrast to many flexible memristor designs, where repeated mechanical cycling gradually degrades the active material through crack formation, atomic displacement, or electromigration. Here, deformation is not damage; it is information. The researchers describe this as a mechanoelectronic design principle, one in which the physical interaction between device and environment becomes the write mechanism for synaptic plasticity.

What makes the comparison to the neuromuscular junction more than a metaphor is the way the device responds to bending. Biological mechanosensitive neurons transduce deformation into graded changes in synaptic strength, with both the amplitude and the dynamics of the response modulated by the magnitude and history of the mechanical stimulus. The hBN memristor reproduces this co-modulation quantitatively. Different bending conditions produce different families of conductance states, and the temporal evolution of those states mirrors the amplitude and dynamics seen in their biological counterparts. In other words, the device does not simply switch between on and off; it exhibits the continuous, history-dependent plasticity that neuroscientists associate with real synapses, where the same stimulus can produce different outcomes depending on what came before.

To demonstrate that these mechanically programmed states are more than a laboratory curiosity, the team put the device to work as an artificial synapse in a computational brain. The conductance values measured under varying bending conditions were used as analog synaptic weights in a fully connected neural network, the workhorse architecture of modern machine learning. The network was trained on MNIST, the classic benchmark of handwritten digit recognition that has served for decades as a proving ground for neuromorphic hardware. Across all bending conditions tested, the network achieved classification accuracy exceeding 91 percent. That figure matters because it shows that the mechanically induced conductance states are not random or noisy; they are stable, distinguishable, and useful as computational elements. A synapse that changes when you bend it, yet still supports high-accuracy inference, is a synapse that could live inside a prosthetic hand or a soft robot.

The implications for flexible and wearable neuromorphics are considerable. Current approaches to tactile sensing in soft electronics typically separate the sensing element from the computing element: a pressure sensor converts touch into an electrical signal, which is then routed to a processor that decides what it means. That separation costs power, latency, and wiring complexity, all of which are precious commodities in a wearable device. A memristor that programs itself through touch collapses that boundary. The sensing and the memory update are the same physical event, happening at the same place at the same time. For applications such as electronic skin, adaptive prosthetics, or soft robotic grippers that must learn from contact, this kind of co-located mechanosensation and plasticity could dramatically simplify the hardware stack.

The choice of materials is also strategically significant. Hexagonal boron nitride is chemically robust, thermally stable, and widely studied as a protective encapsulant and tunneling barrier in two-dimensional electronics. Its layered structure, with weak van der Waals forces between sheets and strong covalent bonds within them, is precisely what enables the sliding mechanism at the heart of the device. The silver and indium tin oxide electrodes provide the vertical conduction path, and the PET substrate supplies the flexibility that makes the whole concept possible. Because the active mechanism does not rely on electrochemical filament formation or oxygen vacancy migration, the usual culprits behind memristor variability and endurance limits, the device sidesteps several long-standing reliability problems in the field.

There are, of course, hurdles between a promising laboratory demonstration and a deployable technology. The reported work establishes the principle and the benchmark performance, but scaling from individual devices to the dense crossbar arrays needed for practical neuromorphic systems will require careful control of device-to-device uniformity, particularly for a mechanism that depends on the precise stacking of exfoliated or grown two-dimensional flakes. Integration with conventional readout circuitry, long-term cycling stability under millions of bending events, and compatibility with large-area manufacturing are all questions that the field will now press forward. The authors acknowledge support from the Science and Engineering Research Board and the Department of Science and Technology in India, reflecting the growing investment in flexible neuromorphic platforms worldwide.

Still, the conceptual shift is hard to overstate. For half a century, computing has treated memory and processing as separate functions and mechanical deformation as an enemy of reliability. This work suggests a future in which the shape of a device is part of its computational state, in which a bend is a bit, and in which soft machines learn the way our muscles do, through the very act of moving. The neuromuscular junction has been quietly optimizing that trick for hundreds of millions of years. With a few atomic layers of boron nitride and a flexible plastic backing, engineers have finally started to take notes.

Subject of Research: Mechanosensitive hexagonal boron nitride memristors for flexible neuromorphic computing

Article Title: Neuromuscular Junction Inspired Mechanosensitive hBN-based Memristors for Flexible Neuromorphics

Article References: Parida, T., Swain, S. K., Sengupta, D., & Kanjilal, A. (2026). Neuromuscular Junction Inspired Mechanosensitive hBN-based Memristors for Flexible Neuromorphics. npj Flexible Electronics. https://doi.org/10.1038/s41528-026-00647-0

Image Credits: AI Generated

DOI: 10.1038/s41528-026-00647-0

Keywords: memristor, hexagonal boron nitride, neuromorphic computing, flexible electronics, neuromuscular junction, synaptic plasticity, mechanosensitive, interlayer sliding, MNIST, wearable devices, two-dimensional materials, artificial synapse

The heart of the device is a tri-layer stack of tungsten diselenide, a semiconductor only a few atoms thick that belongs to the family of transition metal dichalcogenides. Two-dimensional materials like WSe2 are prized in neuromorphic engineering because their entire volume sits at the surface, making their electronic properties exquisitely sensitive to light, electric fields and chemical modification. In their native form, however, WSe2 devices often fall short of the stable, tunable conductance states that artificial synapses demand. The researchers solved this by treating the material with benzyl viologen, a strong electron-donating molecule that donates charge to the WSe2 lattice and shifts its Fermi level, effectively engineering the carrier polarity of the channel and setting the stage for the dramatic optical behaviors that follow.

Before any synaptic claims could be made, the team had to prove the material survived the doping process intact. Atomic force microscopy confirmed the structural integrity and surface morphology of the tri-layer films, while Raman spectroscopy verified that the crystal lattice remained high quality after exposure to the benzyl viologen treatment. On the electrical side, gate-dependent transfer curves and current-voltage measurements mapped out how the doping transformed the device’s behavior, and the researchers constructed an energy band diagram showing the Fermi level shift that the molecular dopants induce. This combination of structural and electrical characterization matters because any degradation of the delicate 2D lattice would undermine the reproducibility that real neuromorphic circuits require.

The most striking results emerge when light hits the device. The BV-doped WSe2 synapse responds strongly to both visible and deep-ultraviolet illumination, but it is the deep-ultraviolet response that gives the device its memory. Under DUV light, charges become trapped within the material system, and these trapped charges persist long after the light is switched off, producing a stable conductance state that functions as a nonvolatile memory. In other words, a flash of ultraviolet light writes information into the device, and the device holds that information without power, much as a biological synapse retains the trace of a past experience. This optically programmable memory is what allows the same physical structure to act simultaneously as a photodetector and a synaptic element.

When stimulated with sequences of optical pulses rather than continuous illumination, the device reproduces hallmark behaviors of biological synapses. Paired-pulse facilitation, a form of short-term plasticity in which the second of two closely spaced signals produces a larger response than the first, appears naturally in the device’s conductance dynamics. With longer or more intense stimulation, the device exhibits long-term potentiation and long-term depression, the persistent strengthening and weakening of connection strength that neuroscientists consider the cellular basis of learning and memory. Crucially, the magnitude of the response is tunable: by controlling the duration of the deep-ultraviolet pulses, the researchers increased the device’s photoresponse from 25.2 percent to 1438.7 percent, a dynamic range wide enough to encode many distinct synaptic weights.

That tunability translates directly into computational capability. The team demonstrated that the device can perform a logic OR gate operation, outputting a high signal when either of two inputs is present, and that it can carry out selective alphabet recognition, distinguishing among written characters based on the optical signatures stored in its conductance states. These demonstrations show that the device is not merely a passive memory element but an active processing unit, capable of the kind of in-memory logic that could dramatically reduce the energy overhead of edge-computing systems, where data must be processed locally under strict power budgets rather than shipped to energy-hungry data centers.

To test whether the device’s synaptic behavior could scale to meaningful machine learning, the researchers implemented its experimentally measured synaptic weight characteristics in an artificial neural network and trained that network to identify handwritten digits from the MNIST dataset, a standard benchmark in computer vision. The simulated network achieved an accuracy of 92 percent, and the team validated the learning process through epoch-dependent learning curves and confusion matrix analysis, confirming that the network’s errors were distributed in the way one expects from a genuinely learning system rather than a lucky configuration. The result is significant because it grounds the device physics in a concrete application: the same conductance modulation that the researchers measured in the laboratory can, in principle, serve as the adjustable weights that artificial neural networks rely on.

The broader significance of the work lies in its multifunctionality. Most neuromorphic devices reported to date specialize in one role, whether as memristors, phase-change memories or electrochemical transistors, and require separate photonic or electronic circuitry to interface with the outside world. By combining optical sensing, nonvolatile charge-trapping memory, short- and long-term plasticity, logic operation and pattern recognition in a single doped 2D material platform, the WSe2 synapse points toward integrated intelligent sensors that could see, remember and compute in one package. Such devices would be natural fits for autonomous drones, biomedical implants and environmental monitors, where every milliwatt saved extends operational life.

There are, of course, hurdles between a laboratory demonstration and deployable technology. The device relies on exquisitely controlled chemical doping of atomically thin crystals, and scaling such processes to wafer-scale manufacturing while maintaining the uniformity that large neural networks demand remains an open challenge in the 2D materials community. The 92 percent MNIST accuracy, while respectable, still trails the best software-based networks, suggesting that device variability and nonlinearity in the conductance updates will need further engineering. The published version of the paper is also an early-release, peer-reviewed accepted manuscript that may undergo further edits before the final version of record appears, so specific figures could be refined.

Even so, the study adds an important entry to the growing catalog of optoelectronic synapses, and its use of benzyl viologen doping to engineer carrier polarity in tri-layer WSe2 gives other researchers a concrete chemical lever to pull. The work was supported by the Korea Basic Science Institute through a National Research Facilities and Equipment Center grant funded by the Korean Ministry of Education, by an IITP Information Technology Research Center grant from the Ministry of Science and ICT, and by the ongoing research funding program of King Saud University in Riyadh. As artificial intelligence migrates from the cloud toward the edge, devices that can be programmed by nothing more than pulses of light, and that learn the way neurons do, may prove to be exactly the kind of hardware the next generation of intelligent machines is waiting for.

Subject of Research: Optoelectronic synaptic devices based on doped two-dimensional tungsten diselenide for neuromorphic computing and logic operations

Article Title: A multifunctional optoelectronic synaptic device for logic operation and neuromorphic computing

Article References: Dastgeer, G., Nisar, S., Fatima, I., Ali, R. F., Rabani, I., Hussain, K., Alsalme, A., & Eom, J. (2026). A multifunctional optoelectronic synaptic device for logic operation and neuromorphic computing. Advanced Composites and Hybrid Materials. https://doi.org/10.1007/s42114-026-02103-z

Image Credits: AI Generated

DOI: 10.1007/s42114-026-02103-z

Keywords: neuromorphic computing, optoelectronic synapse, tungsten diselenide, WSe2, benzyl viologen doping, two-dimensional materials, memristor, synaptic plasticity, charge trapping, artificial neural network, MNIST, logic gates

The central idea is that a gel need not be a passive, homogeneous medium. In a multiphasic gel, the material is structured into distinct phases with different chemical compositions, ionic mobilities and interfacial properties. When an electrical signal is applied, ions do not simply drift through the material in a straight, Ohmic fashion. Instead, they accumulate, deplete and redistribute at the boundaries between phases, giving rise to transient behaviors that depend on the history of the applied voltage. This memory of past stimulation, embedded directly in the spatial distribution of ions, is precisely the property that neuromorphic engineers prize, because it allows a single material element to perform operations that would otherwise require many transistors.

To appreciate why this matters, it helps to consider the limitations of conventional electronics. Silicon transistors move electrons through crystalline semiconductors with extraordinary speed and reliability, but they are fundamentally decoupled from the chemistry of the environments in which they operate. Biological neural systems, by contrast, process signals in wet, soft, salt-laden surroundings, and they do so at energies orders of magnitude lower than digital hardware performing comparable tasks. The commentary by Peng and Zhang situates the new ionic gel work within a broader landscape of iontronic research that has been rapidly consolidating in recent years, including reviews of bioelectronic and iontronic technologies in Nature Reviews Bioengineering and a dedicated iontronics journal literature that has emerged to catalog the field’s accelerating progress.

The physics underlying ionic signal processing is rich and, in some respects, counterintuitive. Because ions are much heavier and slower than electrons, ionic currents respond to voltage changes on time scales ranging from microseconds to seconds, depending on the mobility of the charge carriers, the geometry of the channels and the viscosity of the medium. Far from being a defect, this slowness can be exploited. It introduces natural temporal dynamics, such as gradual charging of interfacial capacitances, diffusion-limited transport and concentration-polarization effects, that mimic the integration and decay behaviors of biological synapses. In a multiphasic gel, the interfaces between phases act as internal barriers and reservoirs, so the effective response of the device becomes a convolution of transport, partitioning and interfacial reaction processes, each with its own characteristic time constant.

Researchers in the field have assembled a substantial body of evidence that such dynamics can be harnessed for computation. A landmark demonstration published in Science in 2023 showed that nanofluidic systems, in which ions are forced through channels comparable in size to the electrical double layers that screen charged surfaces, can display memory and even long-term memory behaviors, providing a physical basis for memristive operation driven entirely by ions. The memristor, a circuit element whose resistance depends on the history of current flow through it, has become the canonical building block of neuromorphic hardware, and its ionic realization suggests that synapse-like elements could be fabricated from soft materials rather than rigid oxide films. Peng and Zhang’s commentary draws on this lineage, emphasizing that the cross-interface dynamics observed in multiphasic gels extend the same conceptual framework into a new class of soft, multiphase media.

Work published in Science in 2024 by Zhang, Tan, Toepfer, Lu and Bayley, several of whom are central figures in the bio-iontronics community, demonstrated that droplet-based ionic architectures can support sophisticated signal processing functions, pointing toward computational primitives built entirely from aqueous phases separated by lipid or polymer membranes. The multiphasic gel described in the Nature Electronics commentary represents a conceptual cousin of these droplet interfacial systems, but with the phases locked into a continuous solid-like scaffold. This distinction matters for applications. Droplet networks are exquisite for fundamental studies but fragile, whereas gels can be handled, patterned and potentially integrated with electrodes, wires and biological tissue, opening a route from laboratory curiosity toward practical soft devices.

The relationship between ionic transport and neuromorphic function can be made concrete by considering what happens when a voltage pulse crosses one of the internal interfaces in the gel. Initially, the ionic current is dominated by the mobile carriers in the phase connected to the driving electrode. As ions pile up at the interface, the local concentration rises, the interfacial electric field redistributes, and counter-diffusion begins to flatten the gradient. If the pulse ends before the system reaches equilibrium, some of the accumulated charge relaxes back, producing a short-term memory trace; if pulses arrive repeatedly, the accumulation can persist, strengthening the effective coupling across the interface in a manner analogous to synaptic facilitation. Two-terminal elements built on this principle naturally implement paired-pulse facilitation, temporal filtering and thresholded switching, all canonical operations of biological synapses.

Chemical reviews published over the past two years have begun to systematize this design space. A comprehensive survey in Chemical Reviews in 2025 examined ionic materials and devices for neuromorphic and bioelectronic applications, while a 2024 review in Chemical Society Reviews mapped the physics of ion transport and rectification in engineered nanostructures. Together with a further Chemical Society Reviews article in 2026 surveying the state of iontronic materials, these works chart a field that is moving from proof-of-concept demonstrations toward systematic design rules, in which the choice of polymer matrix, ionic species, phase composition and interface chemistry can be tuned to produce a desired dynamic response. The multiphasic gel strategy sits squarely within this trend, treating the internal structure of the material itself as an engineering variable.

The companion research article highlighted in the commentary, published by Wu and colleagues in Nature Electronics, provides the experimental substance behind the conceptual discussion. Although the full analytical details are available to readers of the journal, the commentary frames the work as demonstrating that ions in the multiphasic gel exhibit the complex cross-interface dynamics required for neuromorphic processing, and the accompanying figure, titled ‘Ion transporters based on multiphasic gels,’ illustrates the device concept in which ionic transporters, the structural and functional units that shuttle charge between phases, underpin the observed signal processing behavior. Framing a material platform in terms of transporters rather than passive channels reflects a shift in how the field describes ionic devices, emphasizing active, directed, history-dependent charge management rather than simple conduction.

The possible applications of soft ionic processors extend well beyond replacing silicon. Because ionic gels are mechanically compliant, chemically compatible with aqueous environments and operable at low voltages, they are natural candidates for integration with living tissue. Implantable and wearable bioelectronics could, in principle, incorporate local neuromorphic elements that preprocess electrophysiological signals in situ, reducing the bandwidth and power demands on external hardware. Artificial synapses built from multiphasic gels might one day interface directly with neurons, whose native signaling currency is the same ionic flux that the devices process. Researchers have also proposed ionic systems for reservoir computing, in which the rich internal dynamics of a physical medium perform temporal computation, with the gel itself serving as the reservoir. The commentary’s authors, based at a laboratory explicitly dedicated to bio-iontronics, underscore that the convergence of soft materials science, electrochemistry and neural engineering is what gives this direction its momentum.

Significant challenges remain before ionic gels can rival established technologies. Ionic devices are inherently slow compared with electronic ones, which limits their usefulness for high-frequency computation even as it suits them for biological time-scale tasks. Scaling from single junctions to large integrated networks requires reproducible fabrication of multiphase architectures, stable electrode contacts and materials that resist dehydration, fatigue and fouling over long lifetimes. The theoretical toolkit for describing coupled ionic transport across heterogeneous interfaces is still maturing, and researchers acknowledge that the field benefits from continued cross-disciplinary synthesis, as reflected in the breadth of the literature the commentary engages, spanning nanofluidics, memristive physics, droplet interfacial science and bioengineering. Peng and Zhang declare no competing interests, and their perspective, published in September 2026, arrives at a moment when iontronics is acquiring the journals, citations and design principles of a mature discipline.

What the multiphasic gel work ultimately illustrates is that computation need not be confined to crystalline silicon or even to electrons. Matter structured at the mesoscale, with phases that sort, store and release ions across their interfaces, can perform operations that look strikingly neural, and it can do so in a material that is soft, wet and biologically congenial. As the commentary concludes in spirit if not in a single sentence, the complex cross-interface dynamics of ions in such gels are not a nuisance to be engineered away but the very resource from which neuromorphic function is built. If the past decade belonged to memristive oxides and spintronic devices, the next may belong to materials that compute the way life does, one ion at a time, and the ionic gel now stands as one of the clearest demonstrations of that possibility.

Subject of Research: Neuromorphic signal processing using ionic transport across interfaces in a multiphasic gel

Article Title: Neural signal processing in an ionic gel

Article References: Peng, P., & Zhang, Y. (2026). Neural signal processing in an ionic gel. Nature Electronics. https://doi.org/10.1038/s41928-026-01684-3

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01684-3

Keywords: iontronics, multiphasic gel, neuromorphic computing, ionic transport, memristor, soft materials, bioelectronics, nanofluidics, synaptic dynamics, Nature Electronics, Neural, signal