Bioinspired Electronics for Artificial Sensory Systems

Thermosensory Spiking Activity of Proteinoid Microspheres Cross-Linked by Actin Filaments

Actin, found in all eukaryotic cells as globular (G) or filamentous (F) actin, undergoes polymerization, with G-actin units changing shape to become F-actin. Thermal proteins, or proteinoids, are created by heating amino acids (160-200 °C), forming polymeric chains. These proteinoids can swell in an aqueous solution at around 50 °C, producing hollow microspheres filled with a solution, exhibiting voltage spikes. Our research explores the signaling properties of proteinoids, actin filaments, and hybrid networks combining actin and proteinoids. Proteinoids replicate brain excitation dynamics despite lacking specific membranes or ion channels. We investigate enhancing conductivity and spiking by using pure actin, yielding improved coordination in networks compared with individual filaments or proteinoids. Temperature changes (20 short-peptide supramolecular C to 80 °C) regulate conduction states, demonstrating external control over emergent excitability in protobrain systems. Adding actin to proteinoids reduces spike timing variability, providing a more uniform feature distribution. These findings support theoretical models proposing cytoskeletal matrices for functional specification in synthetic protocell brains, promoting stable interaction complexity. The study concludes that life-like signal encoding can emerge spontaneously within biological polymer scaffolds, incorporating abiotic chemistry.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Origami nanogap electrodes for reversible nanoparticle trapping

We present a facile desktop fabrication method for origami-based nanogap indium tin oxide (ITO) electrokinetic particle traps, providing a simplified approach compared to traditional lithographic techniques and effective trapping of nanoparticles. Our approach involves bending ITO thin films on optically transparent polyethylene terephthalate (PET), creating an array of parallel nanogaps. By strategically introducing weak points through cut-sharp edges, we successfully controlled the spread of nanocracks. A single crack spanning the constriction width and splitting the conductive layers forms a nanogap that can effectively trap small nanoparticles after applying an alternating electric potential across the nanogap. We analyze the conditions for reversible trapping and optimal performance of the nanogap ITO electrodes with optical microscopy and electrokinetic impedance spectroscopy. Our findings highlight the potential of this facile fabrication method for the use of ITO at active electro-actuated traps in microfluidic systems.

Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

A Retina-Inspired Optoelectronic Synapse Using Quantum Dots for Neuromorphic Photostimulation of Neurons

Neuromorphic electronics, inspired by the functions of neurons, have the potential to enable biomimetic communication with cells. Such systems require operation in aqueous environments, generation of sufficient levels of ionic currents for neurostimulation, and plasticity. However, their implementation requires a combination of separate devices, such as sensors, organic synaptic transistors, and stimulation electrodes. Here, a compact neuromorphic synapse that combines photodetection, memory, and neurostimulation functionalities all-in-one is presented. The artificial photoreception is facilitated by a photovoltaic device based on cell-interfacing InP/ZnS quantum dots, which induces photo-faradaic charge-transfer mediated plasticity. The device sends excitatory post-synaptic currents exhibiting paired-pulse facilitation and post-tetanic potentiation to the hippocampal neurons via the biohybrid synapse. The electrophysiological recordings indicate modulation of the probability of action potential firing due to biomimetic temporal summation of excitatory post-synaptic currents. The results pave the way for the development of novel bioinspired neuroprosthetics and soft robotics and highlight the potential of quantum dots for achieving versatile neuromorphic functionality in aqueous environments.

Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc

The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.

A Retina-Inspired Optoelectronic Synapse Using Quantum Dots for Neuromorphic Photostimulation of Neurons

Neuromorphic electronics, inspired by the functions of neurons, have the potential to enable biomimetic communication with cells. Such systems require operation in aqueous environments, generation of sufficient levels of ionic currents for neurostimulation, and plasticity. However, their implementation requires a combination of separate devices, such as sensors, organic synaptic transistors, and stimulation electrodes. Here, a compact neuromorphic synapse that combines photodetection, memory, and neurostimulation functionalities all-in-one is presented. The artificial photoreception is facilitated by a photovoltaic device based on cell-interfacing InP/ZnS quantum dots, which induces photo-faradaic charge-transfer mediated plasticity. The device sends excitatory post-synaptic currents exhibiting paired-pulse facilitation and post-tetanic potentiation to the hippocampal neurons via the biohybrid synapse. The electrophysiological recordings indicate modulation of the probability of action potential firing due to biomimetic temporal summation of excitatory post-synaptic currents. These results pave the way for the development of novel bioinspired neuroprosthetics and soft robotics, and highlight the potential of quantum dots for achieving versatile neuromorphic functionality in aqueous environments.

Trainable bioinspired magnetic sensitivity adaptation using ferromagnetic colloidal assemblies

Nature has already suggested bioinspired functions. Beyond them, adaptive and trainable functions could be the inspiration for novel responsive soft matter beyond the state-of-the-art classic static bioinspired, stimulus-responsive, and shape-memory materials. Here, we describe magnetic assembly/disassembly of electrically conducting soft ferromagnetic nickel colloidal particles into surface topographical pillars for bistable electrical trainable memories. They allow magnetic sensing with adaptable and rescalable sensitivity ranges, enabled by bistable memories and kinetic concepts inspired by biological sensory adaptations. Based on the soft ferromagnetism of the nanogranular composition and the resulting rough particle surfaces prepared via a solvothermal synthesis, triggerable structural memory is achieved by the magnetic field-driven particle assembly and disassembly, promoted by interparticle jamming. Electrical conversion from current to frequency for electrical spikes facilitates rescalable and trainable frequency-based sensitivity on magnetic fields. This work suggests an avenue for designing trainable and adaptable life-inspired materials, for example, for soft robotics and interactive autonomous devices.

Electronic Tongues and Noses: A General Overview

As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

A bionic self-driven retinomorphic eye with ionogel photosynaptic retina

Bioinspired bionic eyes should be self-driving, repairable and conformal to arbitrary geometries. Such eye would enable wide-field detection and efficient visual signal processing without requiring external energy, along with retinal transplantation by replacing dysfunctional photoreceptors with healthy ones for vision restoration. A variety of artificial eyes have been constructed with hemispherical silicon, perovskite and heterostructure photoreceptors, but creating zero-powered retinomorphic system with transplantable conformal features remains elusive. By combining neuromorphic principle with retinal and ionoelastomer engineering, we demonstrate a self-driven hemispherical retinomorphic eye with elastomeric retina made of ionogel heterojunction as photoreceptors. The receptor driven by photothermoelectric effect shows photoperception with broadband light detection (365 to 970 nm), wide field-of-view (180°) and photosynaptic (paired-pulse facilitation index, 153%) behaviors for biosimilar visual learning. The retinal photoreceptors are transplantable and conformal to any complex surface, enabling visual restoration for dynamic optical imaging and motion tracking.

Binding-Free Taste Visualization with Plasmonic Metasurfaces

Taste sensors using photonics, termed artificial photonic tongues, have emerged as a promising platform for intuitive taste discrimination. However, the need for complex binding protocols for each taste profile limits their applicability to a narrow range of taste molecules. Here, we introduce an intriguing "binding-free" approach to molecular taste sensing using plasmonics, eliminating the requirement for physical or chemical binding protocols. We develop a wafer-scale plasmonic metasurface constructed by coating metallic nanoparticles in a scalable manner onto a metallic mirror. This metasurface functions to detect molecular refractive indices and surface tensions via 2D projection optical images of an array of liquid droplets containing the taste molecules on top, which can immediately visualize and distinguish between the five basic tastes of molecules (including their mixtures) as well as other additional spicy and alcoholic tastes. We anticipate that this intuitive and rapid taste-sensing approach has the potential to establish a user-friendly and portable taste-sensing platform.

Intuition-and-Tactile Bimodal Sensing Based on Artificial-Intelligence-Motivated All-Fabric Bionic Electronic Skin for Intelligent Material Perception

Developing electronic skins (e-skins) with extraordinary perception through bionic strategies has far-reaching significance for the intellectualization of robot skins. Here, an artificial intelligence (AI)-motivated all-fabric bionic (AFB) e-skin is proposed, where the overall structure is inspired by the interlocked bionics of the epidermis-dermis interface inside the skin, while the structural design inspiration of the dielectric layer derives from the branch-needle structure of conifers. More importantly, AFB e-skin achieves intuition sensing in proximity mode and tactile sensing in pressure mode based on the fringing and iontronic effects, respectively, and is simulated and verified through COMSOL finite element analysis. The proposed AFB e-skin in pressure mode exhibits maximum sensitivity of 15.06 kPa-1 (<50 kPa), linear sensitivity of 6.06 kPa-1 (50-200 kPa), and fast response/recovery time of 5.6 ms (40 kPa). By integrating AFB e-skin with AI algorithm, and with the support of material inference mechanisms based on dielectric constant and softness/hardness, an intelligent material perception system capable of recognizing nine materials with indistinguishable surfaces within one proximity-pressure cycle is established, demonstrating abilities that surpass human perception.

Evolution of Musculoskeletal Electronics

The musculoskeletal system, constituting the largest human physiological system, plays a critical role in providing structural support to the body, facilitating intricate movements, and safeguarding internal organs. By virtue of advancements in revolutionized materials and devices, particularly in the realms of motion capture, health monitoring, and postoperative rehabilitation, "musculoskeletal electronics" has actually emerged as an infancy area, but has not yet been explicitly proposed. In this review, the concept of musculoskeletal electronics is elucidated, and the evolution history, representative progress, and key strategies of the involved materials and state-of-the-art devices are summarized. Therefore, the fundamentals of musculoskeletal electronics and key functionality categories are introduced. Subsequently, recent advances in musculoskeletal electronics are presented from the perspectives of "in vitro" to "in vivo" signal detection, interactive modulation, and therapeutic interventions for healing and recovery. Additionally, nine strategy avenues for the development of advanced musculoskeletal electronic materials and devices are proposed. Finally, concise summaries and perspectives are proposed to highlight the directions that deserve focused attention in this booming field.

Integrating In-Plane Thermoelectricity and Out-Plane Piezoresistivity for Fully Decoupled Temperature-Pressure Sensing

A flexible sensor that simultaneously senses temperature and pressure is crucial in various fields, such as human-machine interaction, artificial intelligence, and biomedical applications. Previous research has mainly focused on single-function flexible sensors for e-skins or smart devices, and integrated bimodal sensing of temperature and pressure without complex crosstalk decoupling algorithms remains challenging. In this work, a flexible bimodal sensor is proposed that utilizes spatial orthogonality between in-plane thermoelectricity and out-plane piezoresistivity, which enables fully decoupled temperature-pressure sensing. The proposed bimodal sensor exhibits a high sensitivity of 281.46 µV K-1 for temperature sensing and 2.181 kPa-1 for pressure sensing. In the bimodal sensing mode, the sensor exhibits negligible mutual interference, providing a measurement error of ± 7% and ± 8% for temperature and pressure, respectively, within a 120 kPa pressure range and a 40 K temperature variation. Additionally, simultaneous spatial mapping of temperature and pressure with a bimodal sensor array enables contact shape identification with enhanced accuracy beyond the limit imposed by the number of sensing units. The proposed integrated bimodal sensing strategy does not require complex crosstalk decoupling algorithms, which represents a significant advancement in flexible sensors for applications that necessitate simultaneous sensing of temperature and pressure.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

An in-sensor humidity computing system for contactless human-computer interaction

Being capable of processing large amounts of redundant data and decreasing power consumption, in-sensor computing approaches play significant roles in neuromorphic computing and are attracting increasing interest in perceptual information processing. Herein, we proposed a high performance humidity-sensitive memristor based on a Ti/graphene oxide (GO)/HfOx/Pt structure and verified its potential for application in remote health management and contactless human-machine interfaces. Since GO possesses abundant hydrophilic groups (carbonyl, epoxide, and hydroxyl), the memristor shows a high humidity sensitivity, fast response, and wide response range. By utilizing the proton-modulated redox reaction, humidity exposure to the memristor induces a dynamic change in the switching between high and low resistance states, ensuring essential synaptic learning functions, such as paired-pulse facilitation, spike number-dependent plasticity, and spike amplitude-dependent plasticity. More importantly, based on the humidity-induced salient features originating from the abundant hydrophilic functional groups in GO, we have implemented a noncontact human-machine interface utilizing the respiratory mode in humans, demonstrating the potential of promoting health monitoring applications and effectively blocking virus transmission. In addition, the high recognition accuracy of contactless handwriting in a 5 × 5 array artificial neural network was successfully achieved, which is attributed to the excellent emulated synaptic behaviors. This study provides a feasible method to develop an excellent humidity-sensitive memristor for constructing efficient in-sensor computing for application in health management and contactless human-computer interaction.

Data-centric artificial olfactory system based on the eigengraph

Recent studies of electronic nose system tend to waste significant amount of important data in odor identification. Until now, the sensitivity-oriented data composition has made it difficult to discover meaningful data to apply artificial intelligence in terms of in-depth analysis for odor attributes specifying the identities of gas molecules, ultimately resulting in hindering the advancement of the artificial olfactory technology. Here, we realize a data-centric approach to implement standardized artificial olfactory systems inspired by human olfactory mechanisms by formally defining and utilizing the concept of Eigengraph in electrochemisty. The implicit odor attributes of the eigengraphs were mathematically substantialized as the Fourier transform-based Mel-Frequency Cepstral Coefficient feature vectors. Their effectiveness and applicability in deep learning processes for gas classification have been clearly demonstrated through experiments on complex mixed gases and automobile exhaust gases. We suggest that our findings can be widely applied as source technologies to develop standardized artificial olfactory systems.

Reconfigurable droplet networks

Droplet networks stabilized by lipid interfacial bilayers or colloidal particles have been extensively investigated in recent years and are of great interest for compartmentalized reactions and biological functions. However, current design strategies are disadvantaged by complex preparations and limited droplet size. Here, by using the assembly and jamming of cucurbit[8]uril surfactants at the oil-water interface, we show a novel means of preparing droplet networks that are multi-responsive, reconfigurable, and internally connected over macroscopic distances. Openings between the droplets enable the exchange of matter, affording a platform for chemical reactions and material synthesis. Our work requires only a manual compression to construct complex patterns of droplet networks, underscoring the simplicity of this strategy and the range of potential applications.

Sniffing Like a Wine Taster: Multiple Overlapping Sniffs (MOSS) Strategy Enhances Electronic Nose Odor Recognition Capability

As highly promising devices for odor recognition, current electronic noses are still not comparable to human olfaction due to the significant disparity in the number of gas sensors versus human olfactory receptors. Inspired by the sniffing skills of wine tasters to achieve better odor perception, a multiple overlapping sniffs (MOSS) strategy is proposed in this study. The MOSS strategy involves rapid and continuous inhalation of odorants to stimulate the sensor array to generate feature-rich temporal signals. Computational fluid dynamics simulations are performed to reveal the mechanism of complex dynamic flows affecting transient responses. The proposed strategy shows over 95% accuracy in the recognition experiments of three gaseous alkanes and six liquors. Results demonstrate that the MOSS strategy can accurately and easily recognize odors with a limited sensor number. The proposed strategy has potential applications in various odor recognition scenarios, such as medical diagnosis, food quality assessment, and environmental surveillance.

Pulsed laser deposition of a Ga2O3 thin film for an optoelectronic synaptic device

With the rapid development of information era, the traditional von Neumann architecture faces the computing bottleneck, and integration of memory and perception is regarded as a potential solution. Herein, a Ga2O3/Si heterojunction based multi-modulated optoelectronic synaptic device is fabricated and demonstrated. As stimulated by ultraviolet (UV) optical spikes, the heterojunction device reveals typical synaptic functions of excitatory-postsynaptic current (EPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), and switch between short-term memory (STM) and long-term memory (LTM). In addition, stronger stimulations like higher reading voltage, stronger optical stimulated intensity, and longer pulse duration time can significantly prolong the attenuation of EPSC, which contributes to the improvement of the forgetting process. Our work provides a potential strategy for future neuromorphic computation through a UV light driven stimulation.

Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics

Owing to the advancement of interdisciplinary concepts, for example, wearable electronics, bioelectronics, and intelligent sensing, during the microelectronics industrial revolution, nowadays, extensively mature wearable sensing devices have become new favorites in the noninvasive human healthcare industry. The combination of wearable sensing devices with bionics is driving frontier developments in various fields, such as personalized medical monitoring and flexible electronics, due to the superior biocompatibilities and diverse sensing mechanisms. It is noticed that the integration of desired functions into wearable device materials can be realized by grafting biomimetic intelligence. Therefore, herein, the mechanism by which biomimetic materials satisfy and further enhance system functionality is reviewed. Next, wearable artificial sensory systems that integrate biomimetic sensing into portable sensing devices are introduced, which have received significant attention from the industry owing to their novel sensing approaches and portabilities. To address the limitations encountered by important signal and data units in biomimetic wearable sensing systems, two paths forward are identified and current challenges and opportunities are presented in this field. In summary, this review provides a further comprehensive understanding of the development of biomimetic wearable sensing devices from both breadth and depth perspectives, offering valuable guidance for future research and application expansion of these devices.

Artificial sensory system based on memristive devices

In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real-time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal-processing functions such as selective adaption in receptors, leaky integrate-and-fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by "artificial receptors," encoded into spike signals via "artificial neurons," and integrated and stored through "artificial synapses." The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high-performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real-time detection and processing of environmental information. This review explores the recent advances in memristor-based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in-depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor-based artificial sensory systems are discussed.

Bioinspired light-driven chloride pump with helical porphyrin channels

Halorhodopsin, a light-driven chloride pump, utilizes photonic energy to drive chloride ions across biological membranes, regulating the ion balance and conveying biological information. In the light-driven chloride pump process, the chloride-binding chromophore (protonated Schiff base) is crucial, able to form the active center by absorbing light and triggering the transport cycle. Inspired by halorhodopsin, we demonstrate an artificial light-driven chloride pump using a helical porphyrin channel array with excellent photoactivity and specific chloride selectivity. The helical porphyrin channels are formed by a porphyrin-core star block copolymer, and the defects along the channels can be effectively repaired by doping a small number of porphyrins. The well-repaired porphyrin channel exhibits the light-driven Cl- migration against a 3-fold concentration gradient, showing the ion pumping behavior. The bio-inspired artificial light-driven chloride pump provides a prospect for designing bioinspired responsive ion channel systems and high-performance optogenetics.

Implantable bioelectrodes: challenges, strategies, and future directions

Implantable bioelectrodes for regulating and monitoring biological behaviors have become indispensable medical devices in modern healthcare, alleviating pathological symptoms such as epilepsy and arrhythmia, and assisting in reversing conditions such as deafness and blindness. In recent years, developments in the fields of materials science and biomedical engineering have contributed to advances in research on implantable bioelectrodes. However, the foreign body reaction (FBR) is still a major constraint for the long-term application of electrodes. In this paper, four types of commonly used implantable bioelectrodes are reviewed, concentrating on their background, development, and a series of complications caused by FBR after long-term implantation. Strategies for resisting FBRs are then devised in terms of physics, chemistry, and nanotechnology. We analyze the major trends in the future development of implantable bioelectrodes and outline some promising research to optimize the long-term operational stability of electrodes. Although current implantable bioelectrodes have been able to achieve good biocompatibility, low impedance, and low mechanical mismatch and trauma, these devices still face the challenge of FBR. Resistance to FBR is still the key for the long-term effectiveness of bioelectrodes, and a better understanding of the mechanisms of FBR, as well as miniaturization, long-term passivation, and coupling with gene therapy may be the way forward for the next generation of implantable bioelectrodes.

Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

A Mitochondrion-Inspired Magnesium-Oxygen Biobattery with High Energy Density In Vivo

Implantable batteries are urgently needed as a power source to meet the demands of the next generation of biomedical electronic devices. However, existing implantable batteries suffer from unsatisfactory energy density, hindering the miniaturization of these devices. Here, a mitochondrion-inspired magnesium-oxygen biobattery that achieves both high energy density and biocompatibility in vivo is reported. The resulting biobattery exhibits a recorded energy density of 2517 Wh L-1 /1491 Wh kg-1 based on the total volume/mass of the device in vivo, which is ≈2.5 times higher than the current state-of-the-art, and can adapt to different environments for stable discharges. The volume of the magnesium-oxygen biobattery can be as thin as 0.015 mm3 and can be scaled up to 400 times larger without reducing the energy density. Additionally, it shows a stable biobattery/tissue interface, significantly reducing foreign body reactions. This work presents an effective strategy for the development of high-performance implantable batteries.

Taste Bud-Inspired Single-Drop Multitaste Sensing for Comprehensive Flavor Analysis with Deep Learning Algorithms

The electronic tongue (E-tongue) system has emerged as a significant innovation, aiming to replicate the complexity of human taste perception. In spite of the advancements in E-tongue technologies, two primary challenges remain to be addressed. First, evaluating the actual taste is complex due to interactions between taste and substances, such as synergistic and suppressive effects. Second, ensuring reliable outcomes in dynamic conditions, particularly when faced with high deviation error data, presents a significant challenge. The present study introduces a bioinspired artificial E-tongue system that mimics the gustatory system by integrating multiple arrays of taste sensors to emulate taste buds in the human tongue and incorporating a customized deep-learning algorithm for taste interpretation. The developed E-tongue system is capable of detecting four distinct tastes in a single drop of dietary compounds, such as saltiness, sourness, astringency, and sweetness, demonstrating notable reversibility and selectivity. The taste profiles of six different wines are obtained by the E-tongue system and demonstrated similarities in taste trends between the E-tongue system and user reviews from online, although some disparities still exist. To mitigate these disparities, a prototype-based classifier with soft voting is devised and implemented for the artificial E-tongue system. The artificial E-tongue system achieved a high classification accuracy of ∼95% in distinguishing among six different wines and ∼90% accuracy even in an environment where more than 1/3 of the data contained errors. Moreover, by harnessing the capabilities of deep learning technology, a recommendation system was demonstrated to enhance the user experience.

Energy Efficient Artificial Olfactory System with Integrated Sensing and Computing Capabilities for Food Spoilage Detection

Artificial olfactory systems (AOSs) that mimic biological olfactory systems are of great interest. However, most existing AOSs suffer from high energy consumption levels and latency issues due to data conversion and transmission. In this work, an energy- and area-efficient AOS based on near-sensor computing is proposed. The AOS efficiently integrates an array of sensing units (merged field effect transistor (FET)-type gas sensors and amplifier circuits) and an AND-type nonvolatile memory (NVM) array. The signals of the sensing units are directly connected to the NVM array and are computed in memory, and the meaningful linear combinations of signals are output as bit line currents. The AOS is designed to detect food spoilage by employing thin zinc oxide films as gas-sensing materials, and it exhibits low detection limits for H2 S and NH3 gases (0.01 ppm), which are high-protein food spoilage markers. As a proof of concept, monitoring the entire spoilage process of chicken tenderloin is demonstrated. The system can continuously track freshness scores and food conditions throughout the spoilage process. The proposed AOS platform is applicable to various applications due to its ability to change the sensing temperature and programmable NVM cells.

An Artificial Olfactory System Based on a Chemi-Memristive Device

Technologies based on the fusion of gas sensors and neuromorphic computing to mimic the olfactory system have immense potential. However, the implementation of neuromorphic olfactory systems remains in a state of infancy because conventional gas sensors lack the necessary functions. Therefore, this study proposes a hysteretic "chemi-memristive gas sensor" based on oxygen vacancy chemi-memristive dynamics that differ from that of conventional gas sensors. After the memristive switching operation, the redox reaction with the external gas molecules is enhanced, resulting in the generation and elimination of oxygen vacancies that induce rapid current changes. In addition, the pre-generated oxygen vacancies enhance the post-sensing properties. Therefore, fast responses, short recovery times, and hysteretic gas response are achieved by the proposed sensor at room temperature. Based on the advantageous functionality of the sensor, device-level olfactory systems that can monitor the history of input gas stimuli are experimentally demonstrated as a potential application. Moreover, analog conductance modulation induced by oxidizing and reducing gases enables the conversion of external gas stimuli into synaptic weights and hence the realization of typical synaptic functionalities without an additional device or circuit. The proposed chemi-memristive device represents an advance in the bioinspired technology adopted in creating artificial intelligence systems.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

Classification-Based Evaluation of Multi-Ingredient Dish Using Graphene-Modified Interdigital Electrodes

A taste sensor with global selectivity can be used to discriminate taste of foods and provide evaluations. Interfaces that could interact with broad food ingredients are beneficial for data collection. Here, we prepared electrochemically reduced graphene oxide (ERGO)-modified interdigital electrodes. The interfaces of modified electrodes showed good sensitivity towards cooking condiments in mixed multi-ingredients solutions under electrochemical impedance spectroscopy (EIS). A database of EIS of cooking condiments was established. Based on the principal component analysis (PCA), subsets of three taste dimensions were classified, which could distinguish an unknown dish from a standard dish. Further, we demonstrated the effectiveness of the electrodes on a typical dish of scrambled eggs with tomato. Our kind of electronic tongue did not measure the quantitation of each ingredient, instead relying on the database and classification algorithm. This method is facile and offers a universal approach to simultaneously identifying multiple ingredients.

A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh-Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.

Flexible Optoelectronic Multimodal Proximity/Pressure/Temperature Sensors with Low Signal Interference

Multimodal tactile sensors are a crucial part of intelligent human-machine interaction and collaboration. Simultaneous detection of proximity, pressure, and temperature on a single sensor can greatly promote the safety, interactivity, and compactness of interaction systems. However, severe signal interference and complex decoupling algorithms hinder the actual applications. Here, this work reports a flexible optoelectronic multimodal sensor capable of detecting and decoupling proximity/pressure/temperature by integrating a light waveguide and an interdigital electrode (IDE) into a compact fibrous sensor. Negligible signal interference is realized by combining heterogeneous sensing mechanisms of optics and electronics, which encodes proximity into capacitance, pressure into light intensity and temperature into resistance. The sensor exhibits a large sensing distance of 225 mm with fast responses for proximity detection, a pressure sensitivity of 0.42 N-1 , and a temperature sensitivity of 7% °C-1 . As a proof of concept, a doll equipped with the sensor can accurately discriminate and detect various stimuli, thus achieving safe and immersive interactions with the user. This work opens up promising paths for self-decoupled multimodal sensors and related human/machine/environment interaction applications.

A nanonewton-scale biomimetic mechanosensor

Biomimetic mechanosensors have profound implications for various areas, including health care, prosthetics, human‒machine interfaces, and robotics. As one of the most important parameters, the sensitivity of mechanosensors is intrinsically determined by the detection resolution to mechanical force. In this manuscript, we expand the force detection resolution of current biomimetic mechanosensors from the micronewton to nanonewton scale. We develop a nanocrack-based electronic whisker-type mechanosensor that has a detection resolution of 72.2 nN. We achieve the perception of subtle mechanical stimuli, such as tiny objects and airflow, and the recognition of surface morphology down to a 30 nm height, which is the finest resolution ever reported in biomimetic mechanosensors. More importantly, we explore the use of this mechanosensor in wearable devices for sensing gravity field orientation with respect to the body, which has not been previously achieved by these types of sensors. We develop a wearable smart system for sensing the body's posture and movements, which can be used for remote monitoring of falls in elderly people. In summary, the proposed device offers great advantages for not only improving sensing ability but also expanding functions and thus can be used in many fields not currently served by mechanosensors.

In Situ Grown Silver-Polymer Framework with Coordination Complexes for Functional Artificial Tissues

Self-sensing actuators are critical to artificial robots with biomimetic proprio-/exteroception properties of biological neuromuscular systems. Existing add-on approaches, which physically blend heterogeneous sensor/actuator components, fall short of yielding satisfactory solutions, considering their suboptimal interfaces, poor adhesion, and electronic/mechanical property mismatches. Here, a single homogeneous material platform is reported by creating a silver-polymer framework (SPF), thus realizing the seamless sensing-actuation unification. The SPF-enabled elastomer is highly stretchable (1200%), conductive (0.076 S m^-1 ), and strong (0.76 MPa in-strength), where the stretchable polymer matrix synthesis and in situ silver nanoparticles reduction are accomplished simultaneously. Benefiting from the multimodal sensing capability from its architecture itself (mechanical and thermal cues), self-sensing actuation (proprio-deformations and external stimuli perceptions) is achieved for the SPF-based pneumatic actuator, alongside an excellent load-lifting attribute (up to 3700 times its own weight), substantiating its advantage of the unified sensing-actuation feature in a single homogenous material. In view of its human somatosensitive muscular systems imitative functionality, the reported SPF bodes well for use with next-generation functional tissues, including artificial skins, human-machine interfaces, self-sensing robots, and otherwise dynamic materials.

Organic Optoelectronic Synapses for Sound Perception

The neuromorphic systems for sound perception is under highly demanding for the future bioinspired electronics and humanoid robots. However, the sound perception based on volume, tone and timbre remains unknown. Herein, organic optoelectronic synapses (OOSs) are constructed for unprecedented sound recognition. The volume, tone and timbre of sound can be regulated appropriately by the input signal of voltages, frequencies and light intensities of OOSs, according to the amplitude, frequency, and waveform of the sound. The quantitative relation between recognition factor (ζ) and postsynaptic current (I = Ilight - Idark) is established to achieve sound perception. Interestingly, the bell sound for University of Chinese Academy of Sciences is recognized with an accuracy of 99.8%. The mechanism studies reveal that the impedance of the interfacial layers play a critical role in the synaptic performances. This contribution presents unprecedented artificial synapses for sound perception at hardware levels.

Free Vibration Analysis of a Tunable Micro-Fabrication Device Comprising Asymmetric L-Shaped Membranes

Membrane sensors have been widely used in various fields owing to their multifunctionality and cost-effectiveness. However, few studies have investigated frequency-tunable membrane sensors, which could enable versatility in the face of different device requirements while retaining high sensitivity, fast response times, and high accuracy. In this study, we propose a device comprising an asymmetric L-shaped membrane with tunable operating frequencies for microfabrication and mass sensing applications. The resonant frequency could be controlled by adjusting the membrane geometry. To fully understand the vibration characteristics of the asymmetric L-shaped membrane, the free vibrations of the membrane are first solved by a semi-analytical treatment combining domain decomposition and variable separation methods. The finite-element solutions confirmed the validity of the derived semi-analytical solutions. Parametric analysis results revealed that the fundamental natural frequency decreases monotonically with the increase in length or width of the membrane segment. Numerical examples revealed that the proposed model can be employed to identify suitable materials for membrane sensors with specific frequency requirements under a given set of L-shaped membrane geometries. The model can also achieve frequency matching by changing the length or width of membrane segments given a specified membrane material. Finally, performance sensitivity analyses for mass sensing were carried out, and the results showed that the performance sensitivity was up to 0.7 kHz/pg for polymer materials under certain conditions.

Recent Advances in Bioinspired Hydrogels: Materials, Devices, and Biosignal Computing

The remarkable ability of biological systems to sense and adapt to complex environmental conditions has inspired new materials and novel designs for next-generation wearable devices. Hydrogels are being intensively investigated for their versatile functions in wearable devices due to their superior softness, biocompatibility, and rapid stimulus response. This review focuses on recent strategies for developing bioinspired hydrogel wearable devices that can accommodate mechanical strain and integrate seamlessly with biological systems. We will provide an overview of different types of bioinspired hydrogels tailored for wearable devices. Next, we will discuss the recent progress of bioinspired hydrogel wearable devices such as electronic skin and smart contact lenses. Also, we will comprehensively summarize biosignal readout methods for hydrogel wearable devices as well as advances in powering and wireless data transmission technologies. Finally, current challenges facing these wearable devices are discussed, and future directions are proposed.

Long-Term Flexible Neural Interface for Synchronous Recording of Cross-Regional Sensory Processing along the Olfactory Pathway

Humans perceive the world through five senses, of which olfaction is the oldest evolutionary sense that enables the detection of chemicals in the external environment. Recent progress in bioinspired electronics has boosted the development of artificial sensory systems. Here, a biohybrid olfactory system is proposed by integrating living mammals with implantable flexible neural electrodes, to employ the outstanding properties of mammalian olfactory system. In olfactory perception, the peripheral organ-olfactory epithelium (OE) projects axons into the olfactory relay station-olfactory bulb (OB). The olfactory information encoded in the neural activity is recorded from both OE and OB simultaneously using flexible neural electrodes. Results reveal that spontaneous slow oscillations (<12 Hz) in both OE and OB closely follow respiration. This respiration-locked rhythm modulates the amplitude of fast oscillations (>20 Hz), which are associated with odor perception. Further, by extracting the characteristics of odor-evoked oscillatory signals, responses of different odors are identified and classified with 80% accuracy. This study demonstrates for the first time that the flexible electrode enables chronic stable electrophysiological recordings of the peripheral and central olfactory system in vivo. Overall, the method provides a novel neural interface for olfactory biosensing and cognitive processing.

Biological function simulation in neuromorphic devices: from synapse and neuron to behavior

As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.

Perspective for soft robotics: the field's past and future

Since its beginnings in the 1960s, soft robotics has been a steadily growing field that has enjoyed recent growth with the advent of rapid prototyping and the provision of new flexible materials. These two innovations have enabled the development of fully flexible and untethered soft robotic systems. The integration of novel sensors enabled by new manufacturing processes and materials shows promise for enabling the production of soft systems with 'embodied intelligence'. Here, four experts present their perspectives for the future of the field of soft robotics based on these past innovations. Their focus is on finding answers to the questions of: how to manufacture soft robots, and on how soft robots can sense, move, and think. We highlight industrial production techniques, which are unused to date for manufacturing soft robots. They discuss how novel tactile sensors for soft robots could be created to enable better interaction of the soft robot with the environment. In conclusion this article highlights how embodied intelligence in soft robots could be used to make soft robots think and to make systems that can compute, autonomously, from sensory inputs.

Flexible Electronics Based on Organic Semiconductors: from Patterned Assembly to Integrated Applications

Organic flexible electronic devices are at the forefront of the electronics as they possess the potential to bring about a major lifestyle revolution owing to outstanding properties of organic semiconductors, including solution processability, lightweight and flexibility. For the integration of organic flexible electronics, the precise patterning and ordered assembly of organic semiconductors have attracted wide attention and gained rapid developments, which not only reduces the charge crosstalk between adjacent devices, but also enhances device uniformity and reproducibility. This review focuses on recent advances in the design, patterned assembly of organic semiconductors, and flexible electronic devices, especially for flexible organic field-effect transistors (FOFETs) and their multifunctional applications. First, typical organic semiconductor materials and material design methods are introduced. Based on these organic materials with not only superior mechanical properties but also high carrier mobility, patterned assembly strategies on flexible substrates, including one-step and two-step approaches are discussed. Advanced applications of flexible electronic devices based on organic semiconductor patterns are then highlighted. Finally, future challenges and possible directions in the field to motivate the development of the next generation of flexible electronics are proposed.

Applications of Graphene in Five Senses, Nervous System, and Artificial Muscles

Graphene remains of great interest in biomedical applications because of biocompatibility. Diseases relating to human senses interfere with life satisfaction and happiness. Therefore, the restoration by artificial organs or sensory devices may bring a bright future by the recovery of senses in patients. In this review, we update the most recent progress in graphene based sensors for mimicking human senses such as artificial retina for image sensors, artificial eardrums, gas sensors, chemical sensors, and tactile sensors. The brain-like processors are discussed based on conventional transistors as well as memristor related neuromorphic computing. The brain-machine interface is introduced for providing a single pathway. Besides, the artificial muscles based on graphene are summarized in the means of actuators in order to react to the physical world. Future opportunities remain for elevating the performances of human-like sensors and their clinical applications.

Optically modulated ionic conductivity in a hydrogel for emulating synaptic functions

Ion-conductive hydrogels, with ions as signal carriers, have become promising candidates to construct functional ionotronics for sensing, actuating, and robotics engineering. However, rational modulation of ionic migration to mimic biological information processing, including learning and memory, remains challenging to be realized in hydrogel materials. Here, we develop a hybrid hydrogel with optically modulated ionic conductivity to emulate the functions of a biological synapse. Through a responsive supramolecular approach, optical stimuli can trigger the release of mobile ions for tuning the conductivity of the hydrogel, which is analogous to the modulation of synaptic plasticity. As a proof of concept, this hydrogel can be used as an information processing unit to perceive different optical stimuli and regulate the grasping motion of a robotic hand, performing logical motion feedback with "learning-experience" function. Our ionic hydrogel provides a valuable strategy toward developing bioinspired ionotronic systems and pushes forward the functional applications of hydrogel materials.

A flexible artificial chemosensory neuronal synapse based on chemoreceptive ionogel-gated electrochemical transistor

The human olfactory system comprises olfactory receptor neurons, projection neurons, and interneurons that perform remarkably sophisticated functions, including sensing, filtration, memorization, and forgetting of chemical stimuli for perception. Developing an artificial olfactory system that can mimic these functions has proved to be challenging. Herein, inspired by the neuronal network inside the glomerulus of the olfactory bulb, we present an artificial chemosensory neuronal synapse that can sense chemical stimuli and mimic the functions of excitatory and inhibitory neurotransmitter release in the synapses between olfactory receptor neurons, projection neurons, and interneurons. The proposed device is based on a flexible organic electrochemical transistor gated by the potential generated by the interaction of gas molecules with ions in a chemoreceptive ionogel. The combined use of a chemoreceptive ionogel and an organic semiconductor channel allows for a long retentive memory in response to chemical stimuli. Long-term memorization of the excitatory chemical stimulus can be also erased by applying an inhibitory electrical stimulus due to ion dynamics in the chemoresponsive ionogel gate electrolyte. Applying a simple device design, we were able to mimic the excitatory and inhibitory synaptic functions of chemical synapses in the olfactory system, which can further advance the development of artificial neuronal systems for biomimetic chemosensory applications.

Retina-Inspired Organic Photonic Synapses for Selective Detection of SWIR Light

Photonic synapses with the dual function of optical signal detection and information processing can simulate human visual system. However, photonic synapses with selective detection of short-wavelength infrared (SWIR) light have never been reported, which can not only broaden the human vision region but also integrate neuromorphic computation and infrared optical communication. Here, organic photonic synapses based on a new donor-acceptor copolymer P1 are fabricated, which exhibit excellent synaptic characteristics with selective detection for SWIR and extremely low energy consumption (2.85 fJ). The working mechanism is rooted in energy level barriers and unbalanced charge transportation. Moreover, these photonic synapses demonstrate excellent performance in multi-signal logic editing, letter imaging and memory with noise reduction function. This contribution provides ideas of constructing selective-response synapses for artificial visual system and neuromorphic computing.

Estimation of Fast and Slow Adaptions in the Tactile Sensation of Mechanoreceptors Mimicked by Hybrid Fluid (HF) Rubber with Equivalent Electric Circuits and Properties

In order to advance engineering applications of robotics such as wearable health-monitoring devices, humanoid robots, etc., it is essential to investigate the tactile sensations of artificial haptic sensors mimicking bioinspired human cutaneous mechanoreceptors such as free nerve endings, Merkel's cells, Krause end bulbs, Meissner corpuscles, Ruffini endings, and Pacinian corpuscles. The generated receptor's potential response to extraneous stimuli, categorized as slow adaption (SA) or fast adaption (FA), is particularly significant as a typical property. The present study addressed the estimation of SA and FA by utilizing morphologically fabricated mechanoreceptors made of our proposed magnetically responsive intelligent fluid, hybrid fluid (HF), and by applying our proposed electrolytic polymerization. Electric circuit models of the mechanoreceptors were generated using experimental data on capacitance and inductance on the basis of the electric characteristics of impedance. The present results regarding equivalent firing rates based on FA and SA are consistent with the FA and SA findings of vital mechanoreceptors by biomedical analysis. The present investigative process is useful to clarify the time of response to a force on the fabricated artificial mechanoreceptor.

A Flexible Tribotronic Artificial Synapse with Bioinspired Neurosensory Behavior

As key components of artificial afferent nervous systems, synaptic devices can mimic the physiological synaptic behaviors, which have attracted extensive attentions. Here, a flexible tribotronic artificial synapse (TAS) with bioinspired neurosensory behavior is developed. The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor, which can tune the carriers transport through the migration/accumulation of ions. The TAS successfully demonstrates a series of synaptic behaviors by external stimuli, such as excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memory process from sensory memory to short-term memory and long-term memory. Moreover, the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm, and the TAS still exhibits excellent durability after 1000 bending cycles. Finally, Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell, respectively. This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems, which is great significance to the practical application of artificial limbs, robotics, and bionics in future.