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

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

A Recyclable Ionogel with High Mechanical Robustness Based on Covalent Adaptable Networks

Ionogels are an emerging class of soft materials for flexible electronics, with high ionic conductivity, low volatility, and mechanical stretchability. Recyclable ionogels are recently developed to address the sustainability crisis of current electronics, through the introduction of non-covalent bonds. However, this strategy sacrifices mechanical robustness and chemical stability, severely diminishing the potential for practical application. Here, covalent adaptable networks (CANs) are incorporated into ionogels, where dynamic covalent crosslinks endow high strength (11.3 MPa tensile strength), stretchability (2396% elongation at break), elasticity (energy loss coefficient of 0.055 at 100% strain), and durability (5000 cycles of 150% strain). The reversible nature of CANs allows the ionogel to be closed-loop recyclable for up to ten times. Additionally, the ionogel is toughened by physical crosslinks between conducting ions and polymer networks, breaking the common dilemma in enhancing mechanical properties and electrical conductivity. The ionogel demonstrates robust strain sensing performance under harsh mechanical treatments and is applied for reconfigurable multimodal sensing based on its recyclability. This study provides insights into improving the mechanical and electrical properties of ionogels toward functionally reliable and environmentally sustainable bioelectronics.

Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced Graphene Oxide and Metal Oxide Nanoparticles via Machine Learning

Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to create wearable devices for detecting hazardous gases surrounding us. However, chemiresistive gas sensors typically suffer from poor selectivity and zero-cross selectivity toward similar types of gases. Herein, a flexible, RT operable chemiresistive gas sensors array is designed, featuring reduced graphene oxide (rGO) and rGO decorated with zinc oxide (ZnO), titanium dioxide (TiO2), and tin dioxide (SnO2) nanoparticles (NPs) on a flexible polyimide (PI) substrate. The sensor array consists of four different sensing layers capable of the selective identification of various hazardous gases such as NO2, NO, and SO2 using machine learning (ML). The gas sensor array exhibits a stable response even when mechanically deformed or exposed to high humidity (up to 60%). Each gas sensor, due to the different metal oxide NPs, shows unique responses in terms of sensitivity, responsiveness, response time, and recovery time to different gases. Consequently, the sensor array generates distinct response patterns that effectively differentiate between the target gases. By leveraging these distinctive recovery patterns and employing a data fusion approach in ML, specific concentrations of target gases can be distinguished. Using ML with fused array sensing data, the training and test accuracies achieved were 98.20 and 97.70%, respectively. This innovative combination of sensor arrays and ML offers significant potential for selective gas detection in environmental monitoring and personal safety applications.

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.

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.

Bioinspired Artificial Visual-Respiratory Synapse as Multimodal Scene Recognition System with Oxidized-Vacancies MXene

In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual-respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and "learning-experience" behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl-preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human-like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human-like mixed-modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.

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.

A Universal Biocompatible and Multifunctional Solid Electrolyte in p-Type and n-Type Organic Electrochemical Transistors for Complementary Circuits and Bioelectronic Interfaces

The development of soft and flexible devices for collection of bioelectrical signals is gaining momentum for wearable and implantable applications. Among these devices, organic electrochemical transistors (OECTs) stand out due to their low operating voltage and large signal amplification capable of transducing weak biological signals. While liquid electrolytes have demonstrated efficacy in OECTs, they limit its operating temperature and pose challenges for electronic packaging due to potential leakage. Conversely, solid electrolytes offer advantages such as mechanical flexibility, robustness against environmental factors, and ability to bridge the interface between rigid dry electronics systems and soft wet biological tissues. However, few systems have demonstrated generality and compatibility with a wide range of state-of-the-art organic mixed ionic-electronic conductors (OMIECs). This paper introduces a highly stretchable, flexible, biocompatible, self-healable gelatin-based solid-state electrolyte, compatible with both p- and n-type OMIEC channels while maintaining high performance and excellent stability. Furthermore, this nonvolatile electrolyte is stable up to 120 °C and exhibits high ionic conductivity even in dry environment. Additionally, an OECT-based complementary inverter with a record-high normalized-gain of 228 V-1 and a corresponding ultralow static power consumption of 1 nW is demonstrated. These advancements pave the way for versatile applications ranging from bioelectronics to power-efficient implants.

Hydrogel-Based Artificial Synapses for Sustainable Neuromorphic Electronics

Hydrogels find widespread applications in biomedicine because of their outstanding biocompatibility, biodegradability, and tunable material properties. Hydrogels can be chemically functionalized or reinforced to respond to physical or chemical stimulation, which opens up new possibilities in the emerging field of intelligent bioelectronics. Here, the state-of-the-art in functional hydrogel-based transistors and memristors is reviewed as potential artificial synapses. Within these systems, hydrogels can serve as semisolid dielectric electrolytes in transistors and as switching layers in memristors. These synaptic devices with volatile and non-volatile resistive switching show good adaptability to external stimuli for short-term and long-term synaptic memory effects, some of which are integrated into synaptic arrays as artificial neurons; although, there are discrepancies in switching performance and efficacy. By comparing different hydrogels and their respective properties, an outlook is provided on a new range of biocompatible, environment-friendly, and sustainable neuromorphic hardware. How potential energy-efficient information storage and processing can be achieved using artificial neural networks with brain-inspired architecture for neuromorphic computing is described. The development of hydrogel-based artificial synapses can significantly impact the fields of neuromorphic bionics, biometrics, and biosensing.

In-Sensor Organic Electrochemical Transistor for the Multimode Neuromorphic Olfactory System

The olfactory system is one of the six basic sensory nervous systems. Developing artificial olfactory systems is challenging due to the complexity of chemical information decoding and memory. Conventional chemical sensors can convert chemical signals into electric signals to decode gas information but they lack memory functions. Additional storage and processing units would significantly increase the complexity and power consumption of the devices, especially for portable and wearable devices. Here, an olfactory-inspired in-sensor organic electrochemical transistor (OI-OECT) is proposed, with the integrated functions of chemical information decoding, tunable memory level, and selectivity of vapor sensing. The ion-gel electrolyte endows the OI-OECT with the function of tunable memory levels and a low operating voltage. Typical synaptic behaviors, including inhibitory postsynaptic current and paired-pulse facilitations, are successfully achieved. Importantly, the gas memory level can be effectively modulated by the gate voltages (0 and -1 V), which realized the transformation of volatile and nonvolatile memory. Furthermore, benefiting from the recognition of multiple gases and ability to detect cumulative damage caused by gases, the OI-OECT is demonstrated for early warning system targeting leakage detection of two gases (NH3 and H2S). This work achieves the integrated functions of chemical gas information decode, tunable gas memory level, and selectivity of gas in a single device, which provides a promising pathway for the development of future artificial olfactory systems.

Recent advances in artificial neuromorphic applications based on perovskite composites

High-performance perovskite materials with excellent physical, electronic, and optical properties play a significant role in artificial neuromorphic devices. However, the development of perovskites in microelectronics is inevitably hindered by their intrinsic non-ideal properties, such as high defect density, environmental sensitivity, and toxicity. By leveraging materials engineering, integrating various materials with perovskites to leverage their mutual strengths presents great potential to enhance ion migration, energy level alignment, photoresponsivity, and surface passivation, thereby advancing optoelectronic and neuromorphic device development. This review initially provides an overview of perovskite materials across different dimensions, highlighting their physical properties and detailing their applications and metrics in two- and three-terminal devices. Subsequently, we comprehensively summarize the application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and crystalline porous materials (CPMs), to develop advanced devices such as memristors, transistors, photodetectors, sensors, light-emitting diodes (LEDs), and artificial neuromorphic systems. Lastly, we outline the challenges and future research directions in synthesizing perovskite composites for neuromorphic devices. Through the review and analysis, we aim to broaden the utilization of perovskites and their composites in neuromorphic research, offering new insights and approaches for grasping the intricate physical working mechanisms and functionalities of perovskites.

A high-performance organic lithium salt-doped OFET with the optical radical effect for photoelectric pulse synaptic simulation and neuromorphic memory learning

Simulation of synaptic characteristics is essential for the application of organic field effect transistors (OFETs) in neural morphology. Although excellent performance, including bias stability and mobility, as well as photoelectric pulse synaptic simulation, has been achieved in SiO2-gated OFETs with PDVT-10 as an organic channel, there are relatively few studies on photoelectric pulse synaptic simulation of electrolyte-gated OFETs based on environmentally friendly and low-voltage operation. Herein, synaptic transistors based on organic semiconductors are reported to simulate the photoelectric pulse response by developing solution-based organic semiconductor PDVT-10, and polyvinyl alcohol (PVA) with an electric double layer (EDL) effect to act as a channel and gate dielectric layer, respectively, and organic lithium salt-doped PVA is used to enhance the EDL effect. The presence of electrical pulses in doped devices not only achieves basic electrical synaptic characteristics, but also significantly realizes the long-term characteristics, pain perception, memory and sensitization applications. Furthermore, the introduction of photoinitiator molecules into the channel layer leads to improved photosynaptic performances by using light-induced free radicals, and the photoelectric synergistic effect has been actualized by introducing heterojunction architecture. This work provides promising prospects for achieving photoelectric pulse modulation based on organic synaptic devices, which shows great potential for the development of artificial intelligence.

Highly Promising 2D/1D BP-C/CNT Bionic Opto-Olfactory Co-Sensory Artificial Synapses for Multisensory Integration

The development of high-performance artificial synaptic neuromorphic devices poses a significant challenge in the creation of biomimetic sensing neural systems that seamlessly integrate both sensory and computational functionalities. In pursuit of this objective, promising bionic opto-olfactory co-sensory artificial synapse devices are constructed utilizing the BP-C/CNT (2D/1D) hybrid filter membrane as the resistive layer. Experimental results demonstrated that the devices seamlessly integrated the light modulation, gas detection, and biological synaptic functions into a single device while addressing the challenge with separating artificial synaptic devices from sensors. These devices offered the following advantages: 1) Simulating visual synapses, they can effectively replicate fundamental synaptic functions under both electrical and optical stimulation. 2) By emulating olfactory synapse responses to specific gases, they can achieve ultra-low detection limits and rapid identification of ethanol and acetone gases. 3) They enable photo-olfactory co-sensing simulations that mimic synaptic function under light-modulated pulse conditions in distinct gas environments, facilitating the study of synaptic learning rules and Pavlovian responses. This work provides a pioneering approach for exploring highly stable 2D BP-based optoelectronics and advancing the development of biomimetic neural systems.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

Thermally and Mechanically Stable Perovskite Artificial Synapse as Tuned by Phase Engineering for Efferent Neuromuscular Control

The doping of perovskites with mixed cations and mixed halides is an effective strategy to optimize phase stability. In this study, we introduce a cubic black phase perovskite CsyFA(1-y)Pb(BrxI(1-x))3 artificial synapse, using phase engineering by adjusting the cesium-bromide content. Low-bromine mixed perovskites are suitable to improve the electric pulse excitation sensitivity and stability of the device. Specifically, the low-bromine and low-cesium mixed perovskite (x = 0.15, y = 0.22) annealed at 373 K allows the device to maintain logic response even after 1000 mechanical flex/flat cycles. The device also shows good thermal stability up to temperatures of 333 K. We have demonstrated reflex-arc behavior with MCMHP synaptic units, capable of making sensory warnings at high frequency. This compositionally engineered, dual-mixed perovskite synaptic device provides significant potential for perceptual soft neurorobotic systems and prostheses.

Autonomous Artificial Olfactory Sensor Systems with Homeostasis Recovery via a Seamless Neuromorphic Architecture

Neuromorphic olfactory systems have been actively studied in recent years owing to their considerable potential in electronic noses, robotics, and neuromorphic data processing systems. However, conventional gas sensors typically have the ability to detect hazardous gas levels but lack synaptic functions such as memory and recognition of gas accumulation, which are essential for realizing human-like neuromorphic sensory system. In this study, a seamless architecture for a neuromorphic olfactory system capable of detecting and memorizing the present level and accumulation status of nitrogen dioxide (NO2) during continuous gas exposure, regulating a self-alarm implementation triggered after 147 and 85 s at a continuous gas exposure of 20 and 40 ppm, respectively. Thin-film-transistor type gas sensors utilizing carbon nanotube semiconductors detect NO2 gas molecules through carrier trapping and exhibit long-term retention properties, which are compatible with neuromorphic excitatory applications. Additionally, the neuromorphic inhibitory performance is also characterized via gas desorption with programmable ultraviolet light exposure, demonstrating homeostasis recovery. These results provide a promising strategy for developing a facile artificial olfactory system that demonstrates complicated biological synaptic functions with a seamless and simplified system architecture.

A pattern recognition artificial olfactory system based on human olfactory receptors and organic synaptic devices

Neuromorphic sensors, designed to emulate natural sensory systems, hold the promise of revolutionizing data extraction by facilitating rapid and energy-efficient analysis of extensive datasets. However, a challenge lies in accurately distinguishing specific analytes within mixtures of chemically similar compounds using existing neuromorphic chemical sensors. In this study, we present an artificial olfactory system (AOS), developed through the integration of human olfactory receptors (hORs) and artificial synapses. This AOS is engineered by interfacing an hOR-functionalized extended gate with an organic synaptic device. The AOS generates distinct patterns for odorants and mixtures thereof, at the molecular chain length level, attributed to specific hOR-odorant binding affinities. This approach enables precise pattern recognition via training and inference simulations. These findings establish a foundation for the development of high-performance sensor platforms and artificial sensory systems, which are ideal for applications in wearable and implantable devices.

Highly Sensitive, Low-Energy-Consumption Biomimetic Olfactory Synaptic Transistors Based on the Aggregation of the Semiconductor Films

Artificial olfactory synaptic devices with low energy consumption and low detection limits are important for the further development of neuromorphic computing and intelligent robotics. In this work, an ultralow energy consumption and low detection limit imitation olfactory synaptic device based on organic field-effect transistors (OFETs) was prepared. The aggregation state of poly(diketopyrrolopyrrole-selenophene) (PTDPP) semiconductor films is modulated by adding unfavorable solvents and annealing treatments to obtain excellent charge transfer and gas synaptic properties. The regulated OFET device can execute basic biological synaptic functions, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term to long-term plasticity, at an ultralow operating voltage of -0.0005 V. The ultralow energy consumption during the biomimetic simulation is in the range of 8.94-88 fJ per spike. Noteworthily, the gas detection limit of the device is as low as 50 ppb, well below normal human NO2 gas perception limits (100-1000 ppb). Additionally, high-pass filtering, Pavlovian conditioned reflexes, and decoding of "Morse code" were simulated. Finally, a grid-free conformal device with outstanding flexibility and stability was fabricated. In conclusion, the control of semiconductor thin-film aggregation provides effective guidance for preparing low-energy-consumption, highly sensitive olfactory nerve-mimicking devices and promoting the development of wearable electronics.

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.

First Demonstration of Yttria-Stabilized Hafnia-Based Long-Retention Solid-State Electrolyte-Gated Transistor for Human-Like Neuromorphic Computing

Electrolyte-gated transistors have strong potential for high-performance artificial synapses in neuromorphic bio-interfaces owing to their outstanding synaptic characteristics, low power consumption, and human-like mechanisms. However, the short retention time is a hurdle to overcome owing to the natural diffusion of protons. Here, a novel modulation technique of ionic conductivity is proposed with yttria-stabilized hafnia for the first time to enhance the retention characteristic of a solid-state electrolyte-gated transistor-based artificial synapse. With the optimization of the ionic conductivity in yttria-stabilized hafnia, a high retention time of over 300 s and remarkable synaptic characteristics are accomplished by regulating channel conductance with precise modulation of the strength of the proton-electron coupling intensity along the input signals. Furthermore, pattern recognition simulation is conducted based on the measured synaptic characteristics, exhibiting 94.41% of operation accuracy, which implies a promising solution for neuromorphic in-memory computing systems with a high operation accuracy and low power consumption.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

Spider-Silk-Inspired Tough, Self-Healing, and Melt-Spinnable Ionogels

As stretchable conductive materials, ionogels have gained increasing attention. However, it still remains crucial to integrate multiple functions including mechanically robust, room temperature self-healing capacity, facile processing, and recyclability into an ionogel-based device with high potential for applications such as soft robots, electronic skins, and wearable electronics. Herein, inspired by the structure of spider silk, a multilevel hydrogen bonding strategy to effectively produce multi-functional ionogels is proposed with a combination of the desirable properties. The ionogels are synthesized based on N-isopropylacrylamide (NIPAM), N, N-dimethylacrylamide (DMA), and ionic liquids (ILs) 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMI][TFSI]). The synergistic hydrogen bonding interactions between PNIPAM chains, PDMA chains, and ILs endow the ionogels with improved mechanical strength along with fast self-healing ability at ambient conditions. Furthermore, the synthesized ionogels show great capability for the continuous fabrication of the ionogel-based fibers using the melt-spinning process. The ionogel fibers exhibit spider-silk-like features with hysteresis behavior, indicating their excellent energy dissipation performance. Moreover, an interwoven network of ionogel fibers with strain and thermal sensing performance can accurately sense the location of objects. In addition, the ionogels show great recyclability and processability into different shapes using 3D printing. This work provides a new strategy to design superior ionogels for diverse applications.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Versatile Neuromorphic Modulation and Biosensing based on N-type Small-molecule Organic Mixed Ionic-Electronic Conductors

The ion/chemical-based modulation feature of organic mixed ionic-electronic conductors (OMIECs) are critical to advancing next generation bio-integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n-type fused all-acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR-Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state-of-art n-type OMIECs based OENS. These superior artificial synapse characteristics of gNR-Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n-type small-molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub-μM level in aqueous electrolyte. This work may open a new path of small-molecule ion-electron conductors for next-generation ANN and bioelectronics.

3D-Printed Intrinsically Stretchable Organic Electrochemical Synaptic Transistor Array

Organic electrochemical transistors (OECTs) for skin-like bioelectronics require mechanical stretchability, softness, and cost-effective large-scale manufacturing. However, developing intrinsically stretchable OECTs using a simple and fast-response technique is challenging due to limitations in functional materials, substrate wettability, and integrated processing of multiple materials. In this regard, we propose a fabrication method devised by combining the preparation of a microstructured hydrophilic substrate, multi-material printing of functional inks with varying viscosities, and optimization of the device channel geometries. The resulting intrinsically stretchable OECT array with synaptic properties was successfully manufactured. These devices demonstrated high transconductance (22.5 mS), excellent mechanical softness (Young's modulus ∼ 2.2 MPa), and stretchability (∼30%). Notably, the device also exhibited artificial synapse functionality, mimicking the biological synapse with features such as paired-pulse depression, short-term plasticity, and long-term plasticity. This study showcases a promising strategy for fabricating intrinsically stretchable OECTs and provides valuable insights for the development of brain-computer interfaces.

Adaptive Memory of a Neuromorphic Transistor with Multi-Sensory Signal Fusion

One of the ultimate goals of artificial intelligence is to achieve the capability of memory evolution and adaptability to changing environments, which is termed adaptive memory. To realize adaptive memory in artificial neuromorphic devices, artificial synapses with multi-sensing capability are required to collect and analyze various sensory cues from the external changing environments. However, due to the lack of platforms for mediating multiple sensory signals, most artificial synapses have been mainly limited to unimodal or bimodal sensory devices. Herein, we present a multi-modal artificial sensory synapse (MASS) based on an organic synapse to realize sensory fusion and adaptive memory. The MASS receives optical, electrical, and pressure information and in turn generates typical synaptic behaviors, mimicking the multi-sensory neurons in the brain. Sophisticated synaptic behaviors, such as Pavlovian dogs, writing/erasing, signal accumulation, and offset, were emulated to demonstrate the joint efforts of bimodal sensory cues. Moreover, associative memory can be formed in the device and be subsequently reshaped by signals from a third perception, mimicking modification of the memory and cognition when encountering a new environment. Our MASS provides a step toward next-generation artificial neural networks with an adaptive memory capability.