The human brain computes with ions. Every thought, memory and movement begins with charged atoms drifting across membranes, accumulating at junctions and triggering cascades of electrochemical activity that no silicon circuit has ever fully replicated. Now, a wave of research in the emerging field of iontronics is asking a deceptively simple question: if biology uses ions to process signals, why should artificial systems not do the same? A recent commentary published in Nature Electronics by Puguang Peng and Yujia Zhang of the Laboratory for Bio-Iontronics at the École Polytechnique Fédérale de Lausanne highlights a striking advance along this path, describing how ions confined within a multiphasic gel can exhibit complex cross-interface dynamics that are suitable for neuromorphic processing, the engineering discipline devoted to building brain-inspired computation.
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
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Denise Maddox. (September 12, 2026). Ionic Gels Bring Neural-Like Signal Processing to Soft Electronics. Scienmag. https://scienmag.com/ionic-gels-bring-neural-like-signal-processing-to-soft-electronics/
Denise Maddox. “Ionic Gels Bring Neural-Like Signal Processing to Soft Electronics.” Scienmag, 12 September 2026, https://scienmag.com/ionic-gels-bring-neural-like-signal-processing-to-soft-electronics/. Accessed 12 September 2026.
Denise Maddox. “Ionic Gels Bring Neural-Like Signal Processing to Soft Electronics.” Scienmag. September 12, 2026. https://scienmag.com/ionic-gels-bring-neural-like-signal-processing-to-soft-electronics/
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