Bioinspired Electronics for Artificial Sensory Systems

Three-Dimensionally Architected Tactile Electronic Skins

Tactile electronic skins (e-skins) are flexible electronic devices that aim to replicate tactile sensing capabilities of the human skin, while possessing skin-like geometric features and materials properties. Since the human skin is composed of complex 3D constructions, where the various types of mechanoreceptors are distributed in a spatial layout, an important trend of tactile e-skin development involves introduction of 3D device architectures that can replicate certain structural features of human skins. The resulting 3D architected e-skins have demonstrated advantages in the detection of shear forces and the decoupled perception of multiple mechanical stimuli, which are of pivotal importance in many application scenarios. In this perspective, we summarize the main biological prototypes of existing 3D architected e-skins, and focus on the key 3D architectures related to tactile sensing capabilities. Then we highlight the enhanced tactile perception of 3D architected e-skins in terms of the super-resolution tactile sensing and predictions of diverse physical properties and surface features of an object, which allow for a broad spectrum of practical applications, such as object recognition, human-machine interactions, dexterous manipulation, and health monitoring. Finally, we discuss scientific challenges and opportunities for future developments of 3D architected tactile e-skins.

Intrinsically Conductive, Highly Compressible, Porous Hydrogel with Exceptional Sensitivity at Low Pressure

Conductive hydrogels have emerged as a promising material in the field of flexible sensing, holding great potential for advanced wearable devices and medical diagnostics, because of their unique conductivity, mechanical deformability, and tissue-like softness. However simultaneously achieving intrinsic conductivity, excellent compressibility and resilience remains a significant challenge. Herein, a novel macroporous, highly compressive, resilient, and intrinsically conductive hydrogel (MPGEL) based on a newly developed easy, eco-friendly, and zero-waste strategy is reported. The MPGEL is prepared using nitrogen as the inert gas and foaming agent, polymerizable Pluronic F127 as a surfactant and crosslinker, and ionic conductive lithium acrylate (LiAA) as the monomer. The resulting MPGEL exhibits highly compressibility and resilience with a low compressive modulus (3.75 kPa), yielding an exceptional compressive sensitivity of 31.67 kPa-1 at low pressure. Therefore, the MPGEL not only can monitor various human movements, but also can effectively detect human cardiac motion, and even precisely distinguish between central and peripheral arterial blood pressure waveforms. This highlights the immense potential of MPGEL for future medical diagnostic technologies and advanced wearable health-monitoring devices.

Ultralow-Power Highly-Selective Near-Infrared (≈850 nm) Carbon Nanotube Flexible Optoelectronic Synaptic Transistors for Real-Time Trajectory Tracking

Optoelectronic synaptic devices are promising candidate components for brain-like efficient neuromorphic computing systems. The development of highly-selective near-infrared (NIR) optoelectronic synaptic devices is important for realizing more efficient optical computing, night monitoring, and robot visual perception. In this work, ultralow-power (56 aJ per light pulse), NIR (≈850 nm) highly-selective optoelectronic synaptic transistor devices based on carbon nanotube thin film transistors are developed by modification of the organic photosensitive material in the device channels. The optoelectronic synaptic devices showed high sensitivity and selectivity to 850 nm pulse light. It is noted that optoelectronic response currents of the optoelectronic synaptic transistor devices after stimulation by a single 850 nm pulse light can be nearly six times higher than those stimulated by single pulse UV light, which is attributed that IHIC has a low bandgap, strong NIR absorption, and ideal energy band alignment with carbon nanotubes. Under pulsed light stimulation, a range of complex synaptic functions are exhibited, including excitatory postsynaptic currents, paired-pulse facilitation, and the transition from short-term plasticity to long-term plasticity, spike-timing-dependent plasticity, and image perception and memory functions. Significantly, the real-time trajectory tracking of the car by the drone under nighttime conditions is successfully simulated using the optoelectronic synaptic transistor array.

Fabrication of a 3D Corneal Model Using Collagen Bioink and Human Corneal Stromal Cells

Corneal transplantation remains a critical treatment option for individuals with corneal disorders, but it faces challenges such as rejection, high associated medical costs, and donor scarcity. A promising alternative for corneal replacement involves fabricating artificial cornea from a patient's own cells. Our study aimed to leverage bioprinting to develop a corneal model using human corneal stromal cells embedded in a collagen-based bioink. We generated both cellular and acellular collagen I (COL I) constructs. Cellular constructs were cultured for up to 4 weeks, and gene expression analysis was performed to assess extracellular matrix (ECM) remodeling and fibrotic markers. Our results demonstrated a significant decrease in the expression of COL I, collagen III (COL III), vimentin (VIM), and vinculin (VCL), indicating a dynamic remodeling process towards a more physiologically relevant corneal ECM. Overall, our study provides a foundational framework for developing customizable, corneal replacements using bioprinting technology. Further research is necessary to optimize the bioink composition and evaluate the functional and biomechanical properties of these bioengineered corneas.

Memristive neuromorphic interfaces: integrating sensory modalities with artificial neural networks

The advent of the Internet of Things (IoT) has led to exponential growth in data generated from sensors, requiring efficient methods to process complex and unstructured external information. Unlike conventional von Neumann sensory systems with separate data collection and processing units, biological sensory systems integrate sensing, memory, and computing to process environmental information in real time with high efficiency. Memristive neuromorphic sensory systems using memristors as their basic components have emerged as promising alternatives to CMOS-based systems. Memristors can closely replicate the key characteristics of biological receptors, neurons, and synapses by integrating the threshold and adaptation properties of receptors, the action potential firing in neurons, and the synaptic plasticity of synapses. Furthermore, through careful engineering of their switching dynamics, the electrical properties of memristors can be tailored to emulate specific functions, while benefiting from high operational speed, low power consumption, and exceptional scalability. Consequently, their integration with high-performance sensors offers a promising pathway toward realizing fully integrated artificial sensory systems that can efficiently process and respond to diverse environmental stimuli in real time. In this review, we first introduce the fundamental principles of memristive neuromorphic technologies for artificial sensory systems, explaining how each component is structured and what functions it performs. We then discuss how these principles can be applied to replicate the four traditional senses, highlighting the underlying mechanisms and recent advances in mimicking biological sensory functions. Finally, we address the remaining challenges and provide prospects for the continued development of memristor-based artificial sensory systems.

Bio-Inspired Ionic Sensors: Transforming Natural Mechanisms into Sensory Technologies

Many natural organisms have evolved unique sensory systems over millions of years that have allowed them to detect various changes in their surrounding environments. Sensory systems feature numerous receptors-such as photoreceptors, mechanoreceptors, and chemoreceptors-that detect various types of external stimuli, including light, pressure, vibration, sound, and chemical substances. These stimuli are converted into electrochemical signals, which are transmitted to the brain to produce the sensations of sight, touch, hearing, taste, and smell. Inspired by the biological principles of sensory systems, recent advancements in electronics have led to a wide range of applications in artificial sensors. In the current review, we highlight recent developments in artificial sensors inspired by biological sensory systems utilizing soft ionic materials. The versatile characteristics of these ionic materials are introduced while focusing on their mechanical and electrical properties. The features and working principles of natural and artificial sensing systems are investigated in terms of six categories: vision, tactile, hearing, gustatory, olfactory, and proximity sensing. Lastly, we explore several challenges that must be overcome while outlining future research directions in the field of soft ionic sensors.

A sensor-actuator-coupled gustatory interface chemically connecting virtual and real environments for remote tasting

Recent advancements in virtual reality (VR) and augmented reality (AR) have strengthened the bridge between virtual and real worlds via human-machine interfaces. Despite extensive research into biophysical signals, gustation, a fundamental component of the five senses, has experienced limited progress. This work reports a bio-integrated gustatory interface, "e-Taste," to address the underrepresented chemical dimension in current VR/AR technologies. This system facilitates remote perception and replication of taste sensations through the coupling of physically separated sensors and actuators with wireless communication modules. By using chemicals representing five basic tastes, systematic codesign of key functional components yields reliable performance including tunability, versatility, safety, and mechanical robustness. Field testing involving human subjects focusing on user perception confirms its proficiency in digitally simulating a range of taste intensities and combinations. Overall, this investigation pioneers a chemical dimension in AR/VR technology, paving the way for users to transcend visual and auditory virtual engagements by integrating the taste sensation into virtual environment for enhanced digital experiences.

An optoelectrochemical synapse based on a single-component n-type mixed conductor

Organic mixed ionic-electronic conductors (OMIECs) are materials that can be used to build bio-inspired electronic devices as they emulate ion-based cellular communication through doping with aqueous ionic charges. The integration of charge photogeneration and electrochemical doping processes in the polymer film enables optoelectronic applications that involve synaptic transistors. However, no OMIEC has yet been implemented to create a miniaturized photoactive platform capable of perceiving and processing multi-spectral visual information. Here, we present a materials and device design concept in which an n-type OMIEC film is incorporated into the micron-scale channel of an electrochemical transistor operating directly in an aqueous electrolyte under ambient conditions. The conjugated polymer channel, consisting of a fluorinated bisistain-lactone-bithiazole acceptor, has a current modulated in response to both electrical and optical stimuli, emulating the multimodal function of the visual nervous system. Our optoelectrochemical synapse achieves multilevel conductance states as well as transduction of visual information covering ultraviolet, visible, and near-infrared regions of the spectrum - a range beyond that of the human visual system's perception. The resulting transistor active-matrix array is capable of adaptive sensing, memory, and pre-processing of visual information, demonstrating an efficient optoelectronic neuromorphic system with multi-task learning capability.

Photonic Nanomaterials for Wearable Health Solutions

This review underscores the transformative potential of photonic nanomaterials in wearable health technologies, driven by increasing demands for personalized health monitoring. Their unique optical and physical properties enable rapid, precise, and sensitive real-time monitoring, outperforming conventional electrical-based sensors. Integrated into ultra-thin, flexible, and stretchable formats, these materials enhance compatibility with the human body, enabling prolonged wear, improved efficiency, and reduced power consumption. A comprehensive exploration is provided of the integration of photonic nanomaterials into wearable devices, addressing material selection, light-matter interaction principles, and device assembly strategies. The review highlights critical elements such as device form factors, sensing modalities, and power and data communication, with representative examples in skin patches and contact lenses. These devices enable precise monitoring and management of biomarkers of diseases or biological responses. Furthermore, advancements in materials and integration approaches have paved the way for continuum of care systems combining multifunctional sensors with therapeutic drug delivery mechanisms. To overcome existing barriers, this review outlines strategies of material design, device engineering, system integration, and machine learning to inspire innovation and accelerate the adoption of photonic nanomaterials for next-generation of wearable health, showcasing their versatility and transformative potential for digital health applications.

Artificial Photothermal Nociceptor Using Mott Oscillators

Bioinspired sensory systems based on spike neural networks have received considerable attention in resolving high energy consumption and limited bandwidth in current sensory systems. To efficiently produce spike signals upon exposure to external stimuli, compact neuron devices are required for signal detection and their encoding into spikes in a single device. Herein, it is demonstrated that Mott oscillative spike neurons can integrate sensing and ceaseless spike generation in a compact form, which emulates the process of evoking photothermal sensing in the features of biological photothermal nociceptors. Interestingly, frequency-tunable and repetitive spikes are generated above the threshold value (Pth = 84 mW cm-2) as a characteristic of "threshold" in leaky-integrate-and-fire (LIF) neurons; the neuron devices successfully mimic a crucial feature of biological thermal nociceptors, including modulation of frequency coding and startup latency depending on the intensity of photothermal stimuli. Furthermore, Mott spike neurons are self-adapted after sensitization upon exposure to high-intensity electromagnetic radiation, which can replicate allodynia and hyperalgesia in a biological sensory system. Thus, this study presents a unique approach to capturing and encoding environmental source data into spikes, enabling efficient sensing of environmental sources for the application of adaptive sensory systems.

Laser-based 3D printing and optical characterization of optical micro-nanostructures inspired by nocturnal insects compound eyes

Nature offers unique examples that help humans produce artificial systems which mimic specific functions of living organisms and provide solutions to complex technical problems of the modern world. For example, the development of 3D micro-nanostructures that mimic nocturnal insect eyes (optimized for night vision), emerges as promising technology for detection in IR spectral region. Here, we report a proof of principle concerning the design and laser 3D printing of all ultrastructural details of nocturnal moth Grapholita Funebrana eyes, for potential use as microlens arrays for IR detection systems. Optimized computer-aided design and laser writing parameters enabled us to reproduce the entire complex architecture of moth compound eyes, with submicrometric spatial accuracy. As such, the laser-imprinted structures consisted in ommatidia-like microstructures with average diameter of about 14 μm, decorated with nipple-like nanopillars between 200 and 400 nm in height and average periodicity of around 450 nm. The dimensions of moth-eye inspired structures deviated by less than 10% from the natural corresponding structures. The optical properties of the moth eyes-inspired microlens arrays were investigated in the infrared (IR) range, between 1000 and 1700 nm. The optical transmission of microlens arrays with nanopillars was up to 17.55% higher than the transmission through microlens arrays without nanopillars. Moreover, the reflection of nanopillar-decorated microlens arrays was up to 0.91% lower than the reflection for microlenses without nanopillars. In addition, the focal spot diameter at 1/e2 for nanopillar-decorated microlens arrays was of 7.64 μm, representing and improvement of 16.5% of focal spot diameter as compared to microlens arrays without nanopillars. Similarly with the IR region, the reflection measured in the Visible range was higher for microlense arrays with nanopillars than the reflection through microlenses arrays without nanopillars. In contrast, in the Visible range the transmission of nanopillar-decorated microlens arrays was lower than the one for microlense arrays without nanopillars, which could be, most likely, assigned to diffraction losses on the nanopillars.

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

Trivalent Ionic Molecular Bridges as Efficient Charge-Trapping Method for All-Solid-State Organic Synaptic Transistors toward Neuromorphic Signal Processing Applications

Achieving high retention of memory state is crucial in artificial synapse devices for neuromorphic computing systems. Of various memorizing methods, a charge-trapping method provides fast response times when it comes to the smallest size of electrons. Here, for the first time, it is demonstrated that trivalent molecular bridges with three ionic bond sites in the polymeric films can efficiently trap electrons in the organic synaptic transistors (OSTRs). A water-soluble polymer with sulfonic acid groups, poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA), is reacted with melamine (ML) to make trivalent molecular bridges with three ionic bond sites for the application of charge-trapping and gate-insulating layer in all-solid-state OSTRs. The OSTRs with the PAMPSA:ML layers are operated at low voltages (≤5 V) with pronounced hysteresis and high memory retention characteristics (ML = 25 mol%) and delivered excellent potentiation/depression performances under modulation of gate pulse frequency. The optimized OSTRs could successfully process analog (Morse/Braile) signals to synaptic current datasets for recognition/prediction logics with an accuracy of >95%, supporting strong potential as all-solid-state synaptic devices for neuromorphic systems in artificial intelligence applications.

A Biomimetic One‐Stone‐Two‐Birds Hydrogel with Electroconductive, Photothermally Antibacterial and Bioadhesive Properties for Skin Tissue Regeneration and Mechanosensation Restoration

Severe skin wounds arising from burns, cancers, and accidents can damage the entire tissue structure, resulting in permanent somatosensory dysfunction in patients. Although emerging hydrogel dressings have shown clinical potential for accelerating wound repair, the use of an individual material to synchronously restore the tissue structure and sensory function of defective skin remains a challenge. Herein, a multifunctional hydrogel that combines electroconductive polydopamine‐capped graphene nanosheets (PrGOs) embedded in a dynamically crosslinked dual‐polysaccharide (xyloglucan and chitosan) matrix network is presented. The fabricated hydrogels have an adjustable modulus that can be matched to skin tissue at the wound site, owing to the dynamic Schiff‐based crosslinking as well as the facile photo‐triggered secondary crosslinking. Furthermore, the photothermal activity of PrGO can elevate the local temperature up to ≈50 °C, significantly restraining bacterial growth. These two factors jointly promote the regeneration of skin tissue. Tissue adhesion of hydrogels is also reported that offers a conformable and robust interface that can detect and quantify human movement and physiological signals to mimic the human skin somatosensory system. This hydrogel offers an effective one‐stone‐for‐two‐birds material that simultaneously achieves tissue regeneration and multi‐signal sensing, promoting the restoration and/or replacement of the structure and function of damaged skins.

Silk Flocked Flexible Sensor Capable of Wide-Range and Sensitive Pressure Perception

In recent years, there have been advancements in high-performance soft sensors with simultaneous moderate sensitivity and wide linearity. However, it remains challenging to combine high-efficiency production and high performance for soft sensors. The skin and hair structure provide an elegantly simple sensing model, where hair acts as signal receptors and basal skin acts as signal processors. Herein, we used efficient electrostatic flocking and extrusion printing to engineer comb-shaped electrodes with conductive polydimethylsiloxane (PDMS) flocked with conductive silk fibers as a biomimetic flexible sensor. The mechanical and electrical properties of modified PDMS and silk fibers were characterized to optimize the functionalization process. The sensor unit exhibited a high linear range up to 2,000 kPa and a competitively good sensitivity of 0.0285 kPa-1 in the contact mode with conductive materials, as well as good resolution in the noncontact mode. Such sensors and a sensor array demonstrated potential applications for detecting pressure disturbances from acoustic activity and for human-robot interactions. We anticipate that the straightforward design and facile fabrication of soft sensors with vertical fibrous morphology for perception will open new avenues for the next generation of high-performance soft sensors integrated with artificial intelligence.

Ultralow-Energy-Consumption Photosynaptic Transistor Utilizing Conjugated Polymers/Perovskite Quantum Dots Nanocomposites With Ligand Density Optimization

The photosynaptic transistor stands as a promising contender for overcoming the von Neumann bottleneck in the realm of photo-communication. In this context, photonic synaptic transistors is developed through a straightforward solution process, employing an organic semiconducting polymer with pendant-naphthalene-containing side chains (PDPPNA) in combination with ligand-density-engineered CsPbBr3 perovskite quantum dots (PQDs). This fabrication approach allows the devices to emulate fundamental synaptic behaviors, encompassing excitatory postsynaptic current, paired-pulse facilitation, the transition from short-to-long-term memory, and the concept of "learning experience." Notably, the phototransistor, incorporating the blend of the PDPPNA and CsPbBr3 PQDs washed with ethyl acetate, achieved an exceptional memory ratio of 104. Simultaneously, the same device exhibited an impressive paired-pulse facilitation ratio of 223% at a moderate operating voltage of -4 V and an extraordinarily low energy consumption of 0.215 aJ at an ultralow operating voltage of -0.1 mV. Consequently, these low-voltage synaptic devices, constructed with a pendant side-chain engineering of organic semiconductors and a ligand density engineering of PQDs through a simple fabrication process, exhibit substantial potential for replicating the visual memory capabilities of the human brain.

A Multimodal Perception-Enabled Flexible Memristor with Combined Sensing-Storage-Memory Functions for Enhanced Artificial Injury Recognition

With the continuous advancement of wearable technology and advanced medical monitoring, there is an increasing demand for electronic devices that can adapt to complex environments and have high perceptual sensitivity. Here, a novel artificial injury perception device based on an Ag/HfOx/ITO/PET flexible memristor is designed to address the limitations of current technologies in multimodal perception and environmental adaptability. The memristor exhibits excellent resistive switching (RS) performance and mechanical flexibility under different bending angles (BAs), temperatures, humid environment, and repetitive folding conditions. Further, the device demonstrates the multimodal perception and conversion capabilities toward voltage, mechanical, and thermal stimuli through current response tests under different conditions, enabling not only the simulation of artificial injury perception but also holds promise for monitoring and controlling the movement of robotic arms. Moreover, the logical operation capability of the memristor-based reconfigurable logic (MRL) gates is also demonstrated, proving the device has great potential applications with sensing, storage, and memory functions. Overall, this study not only provides a direction for the development of the next-generation flexible multimodal sensors, but also has significant implications for technological advancements in many fields such as robotic arms, electronic skin (e-skin), and medical monitoring.

An evaluation of recent advancements in biological sensory organ-inspired neuromorphically tuned biomimetic devices

In the field of neuroscience, significant progress has been made regarding how the brain processes information. Unlike computer processors, the brain comprises neurons and synapses instead of memory blocks and transistors. Despite advancements in artificial neural networks, a complete understanding concerning brain functions remains elusive. For example, to achieve more accurate neuron replication, we must better understand signal transmission during synaptic processes, neural network tunability, and the creation of nanodevices featuring neurons and synapses. This study discusses the latest algorithms utilized in neuromorphic systems, the production of synaptic devices, differences between single and multisensory gadgets, recent advances in multisensory devices, and the promising research opportunities available in this field. We also explored the ability of an artificial synaptic device to mimic biological neural systems across diverse applications. Despite existing challenges, neuroscience-based computing technology holds promise for attracting scientists seeking to enhance solutions and augment the capabilities of neuromorphic devices, thereby fostering future breakthroughs in algorithms and the widespread application of cutting-edge technologies.

Leveraging volatile memristors in neuromorphic computing: from materials to system implementation

Inspired by the functions of biological neural networks, volatile memristors are essential for implementing neuromorphic computing. These devices enable large-scale and energy-efficient data processing by emulating neural functionalities through dynamic resistance changes. The threshold switching characteristics of volatile memristors, which are driven by various mechanisms in materials ranging from oxides to chalcogenides, make them versatile and suitable for neuromorphic computing systems. Understanding these mechanisms and selecting appropriate devices for specific applications are crucial for optimizing the performance. However, the existing literature lacks a comprehensive review of switching mechanisms, their compatibility with different applications, and a deeper exploration of the spatiotemporal processing capabilities and inherent stochasticity of volatile memristors. This review begins with a detailed analysis of the operational principles and material characteristics of volatile memristors. Their diverse applications are then explored, emphasizing their role in crossbar arrays, artificial receptors, and neurons. Furthermore, the potential of volatile memristors in artificial inference systems and reservoir computing is discussed, due to their spatiotemporal processing capabilities. Hardware security applications and probabilistic computing are also examined, where the inherent stochasticity of the devices can improve the system robustness and adaptability. To conclude, the suitability of different switching mechanisms for various applications is evaluated, and future perspectives for the development and implementation of volatile memristors are presented. This review aims to fill the gaps in existing research and highlight the potential of volatile memristors to drive innovation in neuromorphic computing, paving the way for more efficient and powerful computational paradigms.

Combined optical and electrical control of a low-power consuming (∼fJ) two-terminal organic artificial synapse for associative learning and neuromorphic applications

Optoelectronic synaptic devices outperform electrical synapses in speed, energy efficiency, and integration density. Recent progress in visual sensing and optogenetics has led to the integration of light-sensitive materials in these devices, promising unmatched speed, connectivity, and bandwidth. Here, we present a copper phthalocyanine (CuPc) based optoelectronic synaptic device boasting femto Joule power consumption stable at room temperature. The optoelectronic synapse can be operated with energy consumption as low as 430.4 fJ which is very attractive from the point of view of low-power neuromorphic devices. By modulating light pulses, the neuromorphic behavior can be emulated including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), transitioning from short-term plasticity (STP) to long-term plasticity (LTP), spike-rate dependent plasticity (SRDP) and spike-number dependent plasticity (SNDP), etc. Optical potentiation and electrical depression are observed with combined optical and electrical stimulation, proving the multi-functionality of the synapse. Furthermore, the device demonstrates classical associative learning behaviors like Pavlovian conditioning using optical and electrical stimuli. We have established the pain conditioning processes such as hyperalgesic response and pain extinction effects with varying optical pulse amplitudes. These results render the CuPc-based devices as multifunctional and highly versatile artificial synaptic devices for future computing applications, offering unprecedented efficiency and functionality in neuromorphic systems.

Artificial olfactory memory system based on conductive metal-organic frameworks

The olfactory system can generate unique sensory memories of various odorous molecules, guiding emotional and cognitive decisions. However, most existing electronic noses remain constrained to momentary concentration, failing to trigger specific memories for different smells. Here, we report an artificial olfactory memory system utilizing conductive metal-organic frameworks (Ce-HHTP) that integrates sensing and memory and exhibits short- and long-term memory responses to alcohols and aldehydes. Experiments and theoretical calculations show that distinct memories are derived from the specific combinations of Ce-HHTP with O atoms in different guest. An unmanned aircraft equipped with this system realized the sensory memories in established areas. Moreover, the fusion of portable detection boxes and wearable flexible electrodes demonstrated the immense potential in off-site pollution monitoring and health management. This work represents an artificial olfactory memory system with two specific sensory memories under simultaneous conditions, laying the foundation for bionic design with qualities of human olfactory memory.

High-Yield Production of Solution-Processed Highly Robust Organic Artificial Synapses by Thermal Treatments

A promising approach for implementing biomimetic systems relies on organic electronic devices designed to emulate neural synapses. However, organic artificial synapses face challenges in achieving high yield and robustness, rendering them difficult to use in practical applications. In this work, a high-yield and highly stable bulk heterojunction (BHJ) synaptic device composed of Poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) was fabricated via a simple solution process followed by thermal treatments. The crystallinity of P3HT and the precipitation of PCBM in BHJ films can be controlled by the thermal annealing temperatures. At 80 °C, P3HT reaches its highest crystallinity, while PCBM remains uniformly distributed. This thermal treatment significantly contributes to the fabrication of devices characterized by a high yield rate, reaching 98.43%. Additionally, this device remained operational even after being immersed in deionized water, ethanol, and seawater for 100 h. More importantly, it exhibited high elasticity over a wide temperature range from -90 to 310 °C. Finally, this device was utilized to construct a biomimetic vehicle with autonomous memory learning capabilities. After repeated training, the avoidance time was optimized by 31.4%. The robust P3HT:PCBM artificial synapses hold great promise for advancing the development of biomimetic electronic products in extreme environments.

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.

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.

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.

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.

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.

Bioinspired magnetic cilia: from materials to applications

Microscale and nanoscale cilia are ubiquitous in natural systems where they serve diverse biological functions. Bioinspired artificial magnetic cilia have emerged as a highly promising technology with vast potential applications, ranging from soft robotics to highly precise sensors. In this review, we comprehensively discuss the roles of cilia in nature and the various types of magnetic particles utilized in magnetic cilia; additionally, we explore the top-down and bottom-up fabrication techniques employed for their production. Furthermore, we examine the various applications of magnetic cilia, including their use in soft robotics, droplet and particle control systems, fluidics, optical devices, and sensors. Finally, we present our conclusions and the future outlook for magnetic cilia research and development, including the challenges that need to be overcome and the potential for further integration with emerging technologies.

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.