Researchers create an iontronic artificial skin that senses pressure, temperature, and current while learning patterns through built-in memory and computation.
(Nanowerk Spotlight) Efforts to reproduce the sensory functions of human skin have pushed materials science, bioelectronics, and robotics toward more integrated interfaces between machines and their environments. Human skin is not just a flexible barrier; it is a complex organ that senses a wide array of stimuli and retains memory of tactile events. Artificial equivalents have often been constrained by narrow functionality, mechanical rigidity, or dependence on separate computational systems. While recent work in soft materials and neuromorphic engineering has offered partial solutions, most existing systems fall short of replicating the simultaneous sensory and memory capabilities of skin.
Electronic skin systems today typically separate sensing from computing, using arrays of sensors connected to external processors. This creates delays and requires bulky infrastructure. Some neuromorphic devices can mimic neural learning but are usually made of rigid, brittle materials that lack compatibility with soft and flexible surfaces. These systems also require dry and stable environments, limiting real-world applications.
Soft ion-conducting materials, particularly polyionic liquid elastomers, present an alternative. These materials, made of crosslinked polymer networks containing mobile ions, conduct signals in ways similar to biological tissues. Unlike traditional hydrogels, they are solvent-free, stable across wide temperature ranges, and resist drying and freezing. These properties open up new opportunities for artificial skins that combine sensing, memory, and mechanical resilience in a single layer.
Building on these developments, a team of researchers from the Chinese Academy of Sciences and the University of Hong Kong has created an artificial skin that draws direct inspiration from electric eels. These animals use specialized cells called electrocytes to generate and transmit electrical signals for navigation and defense. Mimicking this biological principle, the researchers constructed a layered structure using two oppositely charged polymeric materials—PolyAT and PolyES—to form an iontronic p-n junction. This junction behaves similarly to a diode but with ions instead of electrons, enabling stable, bidirectional signal flow triggered by external stimuli.
The artificial skin detects temperature between –80 and 120 degrees Celsius, pressure from 0.075 pascals to 400 kilopascals, and electrical currents down to 1 nanoampere. These ranges far exceed the capabilities of most current artificial skins, especially those based on hydrogel materials. The device responds dynamically to stimuli, exhibiting behaviors that resemble biological synaptic functions. When stimulated repeatedly, it strengthens its response and retains that state for extended periods, similar to long-term memory in neurons.
a) Electric eels. The first inset below shows the arrangement of electrocytes within the electric organs of electrophorus. The second inset below shows ion fluxes in the firing state. b) Structure of electric receptor in electrophorus. c) Mechanism of voltage generation in electrocytes. d) Schematic diagram of bio-inspired artificial skin integrated on robot fingers for multimodal perception and memory. e) The structure of artificial skin. f) PIL elastomer form a heterojunction by balancing two concurrent processes. (Image: Reprinted with permission from Wiley-VCH Verlag) (click on image to enlarge)
The material displays a range of memory phenomena. It reacts to repeated electrical or mechanical pulses with increasing voltage output, which then decays over time—emulating short-term memory. When exposed to more intense or longer stimulation, the signal persists far longer, modeling long-term memory. The skin also exhibits paired-pulse facilitation, where a second stimulus shortly after the first produces a stronger response, reflecting patterns observed in biological synapses.
Tactile sensing is highly sensitive. Light touches such as contact with silk fabric, applying pressures around 0.075 pascals, elicit a measurable signal. The response intensifies with pressure and adapts over repeated interactions, demonstrating mechanical memory. The skin detects not just whether something touches it but how firmly and how often. It distinguishes between short taps and sustained force. Notably, it maintains these properties after prolonged storage, mechanical deformation, and exposure to high or low temperatures.
Thermal sensing is similarly responsive. Without physical contact, the material can detect temperature changes by registering shifts in ionic mobility. This enables applications such as early warning systems, where heat sources can be identified before contact. The device also shows reliable performance after weeks of use and exposure to varying humidity levels, suggesting good long-term stability.
To extend these capabilities, the researchers built an 8×8 sensor array from the material. Each cell acts independently, allowing spatial mapping of pressure and temperature. When an object of known mass or temperature is placed on the surface, its location and characteristics can be inferred from the pattern of electrical signals generated. In one demonstration, the array was mounted on a robotic hand. The robot could identify objects based on both weight and temperature and selectively grasp those that met specific criteria. This task replicates the human skin’s role in informing decisions about whether an object is safe or appropriate to touch.
Beyond sensing, the artificial skin serves as a platform for reservoir computing—a form of machine learning that processes temporal input patterns. Here, each unit of the skin acts as a dynamic memory cell. By feeding in sequences of electrical pulses representing binary images, the researchers trained the system to classify digits from the MNIST dataset, a standard benchmark of 70,000 handwritten numbers used to evaluate image recognition systems. Using a network of 110 such units, the device achieved 91.3% accuracy, slightly outperforming a software-based equivalent. This was made possible by the material’s ability to exhibit different conductance states based on the timing and intensity of the electrical input.
The significance of this work lies in the integration of perception, memory, and computation into a single stretchable and biocompatible material. Rather than routing data through sensors and external processors, the material handles signal interpretation internally. This reduces latency and energy consumption and is especially relevant for edge computing systems that need to operate independently. The solvent-free ionic design also resists drying and remains functional under freezing or high-heat conditions, making it viable for outdoor use, wearable electronics, and soft robotics.
By combining biologically inspired signal processing with the mechanical and environmental durability of ionic elastomers, the team has demonstrated a sensory material that behaves more like skin than previous technologies. It not only feels, but remembers, adapts, and learns—all without requiring rigid electronics or remote processing. This approach could support more autonomous machines, smarter prosthetics, and more responsive human-machine interfaces.
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