Visualized in-sensor computing.

In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

Vertically Integrated Monolithic Neuromorphic Nanowire Device for Physiological Information Processing.

This study demonstrates the conceptual design and fabrication of a vertically integrated monolithic (VIM) neuromorphic device. The device comprises an n-type SnO2 nanowire bottom channel connected by a shared gate to a p-type P3HT nanowire top channel. This architecture establishes two distinct neural pathways with different response behaviors. The device generates excitatory and inhibitory postsynaptic currents, mimicking the corelease mechanism of bilingual synapses. To enhance the signal processing efficiency, we employed a bipolar spike encoding strategy to convert fluctuating sensory signals to spike trains containing positive and negative pulses. Utilizing the neuromorphic platform for synaptic processing, physiological signals featuring bidirectional fluctuations, including electrocardiogram and breathing signals, can be classified with an accuracy of over 90%. The VIM device holds considerable promise as a solution for developing highly integrated neuromorphic hardware for healthcare and edge intelligence applications.

Neuromorphic antennal sensory system.

Insect antennae facilitate the nuanced detection of vibrations and deflections, and the non-contact perception of magnetic or chemical stimuli, capabilities not found in mammalian skin. Here, we report a neuromorphic antennal sensory system that emulates the structural, functional, and neuronal characteristics of ant antennae. Our system comprises electronic antennae sensor with three-dimensional flexible structures that detects tactile and magnetic stimuli. The integration of artificial synaptic devices adsorbed with solution-processable MoS2 nanoflakes enables synaptic processing of sensory information. By emulating the architecture of receptor-neuron pathway, our system realizes hardware-level, spatiotemporal perception of tactile contact, surface pattern, and magnetic field (detection limits: 1.3 mN, 50 µm, 9.4 mT). Vibrotactile-perception tasks involving profile and texture classifications were accomplished with high accuracy (> 90%), surpassing human performance in "blind" tactile explorations. Magneto-perception tasks including magnetic navigation and touchless interaction were successfully completed. Our work represents a milestone for neuromorphic sensory systems and biomimetic perceptual intelligence.

Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement.

Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different sensory modalities. By mimicking the multisensory integration of ocular-vestibular cues for enhanced spatial perception in macaques, a bioinspired motion-cognition nerve based on a flexible multisensory neuromorphic device is demonstrated. A fast, scalable and solution-processed fabrication strategy is developed to prepare a nanoparticle-doped two-dimensional (2D)-nanoflake thin film, exhibiting superior electrostatic gating capability and charge-carrier mobility. The multi-input neuromorphic device fabricated using this thin film shows history-dependent plasticity, stable linear modulation, and spatiotemporal integration capability. These characteristics ensure parallel, efficient processing of bimodal motion signals encoded as spikes and assigned with different perceptual weights. Motion-cognition function is realized by classifying the motion types using mean firing rates of encoded spikes and postsynaptic current of the device. Demonstrations of recognition of human activity types and drone flight modes reveal that the motion-cognition performance match the bio-plausible principles of perceptual enhancement by multisensory integration. Our system can be potentially applied in sensory robotics and smart wearables.

Neuromorphic Gustatory System with Salt-Taste Perception, Information Processing, and Excessive-Intake Warning Capabilities.

Emulation of the process of a biological gustatory system could benefit the reconstruction of sense of taste. Here we demonstrate the first neuromorphic gustatory system that emulates the ability of taste perception, information processing, and excessive-intake warning functions. The system integrates a chitosan-derived ion-gel sensor, SnO2 nanowire artificial synapses, and an effect-executive unit. The system accomplish perception and encoding behaviors for taste stimulation without using complex circuits and multivariate analysis, showing short response delay (<1 s), long taste memory duration (>2 h), and a wide perceptive concentration range (0.02-6 wt % salt solution). Especially, SnO2 NW artificial synapses have extremely small response voltage (1 mV), exceeding the biological level by orders of magnitude, representing so-far the highest sensitivity record. This work provides a promising strategy to develop bioinspired and biointegrated electronics with the intention of mimicking and restoring the functions of biological sensory systems.

A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation.

Brain-inspired electronics require artificial synapses that have ultra-low energy consumption, high operating speed, and stable flexibility. Here, we demonstrate a flexible artificial synapse that uses a rapidly crystallized perovskite layer at room temperature. The device achieves a series of synaptic functions, including logical operations, temporal and spatial rules, and associative learning. Passivation using phenethyl-ammonium iodide eliminated defects and charge traps to reduce the energy consumption to 13.5 aJ per synaptic event, which is the world record for two-terminal artificial synapses. At this ultralow energy consumption, the device achieves ultrafast response frequency of up to 4.17 MHz; which is orders of magnitude magnitudes higher than previous perovskite artificial synapses. A multi-stimulus accumulative artificial neuromuscular system was then fabricated using the perovskite synapse as a key processing unit to control electrochemical artificial muscles, and realized muscular-fatigue warning. This artificial synapse will have applications in future bio-inspired electronics and neurorobots.

Attention-Shared Multi-Agent Actor-Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks.

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor-critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.

A Flexible Artificial Sensory Nerve Enabled by Nanoparticle-Assembled Synaptic Devices for Neuromorphic Tactile Recognition.

Tactile perception enabled by somatosensory system in human is essential for dexterous tool usage, communication, and interaction. Imparting tactile recognition functions to advanced robots and interactive systems can potentially improve their cognition and intelligence. Here, a flexible artificial sensory nerve that mimics the tactile sensing, neural coding, and synaptic processing functions in human sensory nerve is developed to achieve neuromorphic tactile recognition at device level without relying on algorithms or computing resources. An interfacial self-assembly technique, which produces uniform and defect-less thin film of semiconductor nanoparticles on arbitrary substrates, is employed to prepare the flexible synaptic device. The neural facilitation and sensory memory characteristics of the proton-gating synaptic device enable this system to identify material hardness during robotic grasping and recognize tapping patterns during tactile interaction in a continuous, real-time, high-accuracy manner, demonstrating neuromorphic intelligence and recognition capabilities. This artificial sensory nerve produced in wearable and portable form can be readily integrated with advanced robots and smart human-machine interfaces for implementing human-like tactile cognition in neuromorphic electronics toward robotic and wearable applications.

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network.

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.

A high-resolution, ultrabroad-range and sensitive capacitive tactile sensor based on a CNT/PDMS composite for robotic hands.

Tactile sensors are of great significance for robotic perception improvement to realize stable object manipulation and accurate object identification. To date, developing a broad-range tactile sensor array with high sensitivity economically remains a critical challenge. In this study, a flexible capacitive tactile sensor array, consisting of a carbon nanotube (CNT)/polydimethylsiloxane (PDMS) film, parylene films, and two polyimide (PI) films patterned with electrodes, is facilely prepared. The CNT/PDMS film, acting as a giant dielectric permittivity material, is utilized to improve the sensitivity, while the parylene film serves as the scaffold architecture to extend the working range of the tactile sensor array. Also, it is promising to realize mass production for this sensor array due to the scalable fabrication procedure. The as-prepared sensor exhibits excellent sensing performance with a high sensitivity of 1.61% kPa-1 (<1 MPa), an ultra-broad pressure working range of 0.9 kPa-2.55 MPa, an outstanding durability, a stability up to 5000 cycles, and a fast response time. By integrating our tactile sensor array with a robotic gripper, we show that robots can successfully differentiate object shapes and manipulate light and heavy objects with a closed-loop pressure feedback, demonstrating its great potential in robotic perception and wearable applications.

Digitally aligned ZnO nanowire array based synaptic transistors with intrinsically controlled plasticity for short-term computation and long-term memory.

Digitally aligned long continuous ZnO NWs with distinct widths and microstructures are prepared and used for tuning the plasticity of synaptic transistors (STs) for the first time. Intrinsically controlled synaptic plasticity, i.e. short-term plasticity (STP) and long-term plasticity (LTP), was achieved using the same source material and post-fabrication condition for the first time, which is essential for simple and low-cost fabrication. Moreover, these versatile properties of ZnO STs enable the integration of STP and LTP as realized by multiplexed neurotransmission of different neurotransmitters: dopamine and acetylcholine, which promote learning and memory in organisms, so the device may utilize these processes in neuroelectronic devices. Devices with well-controlled synaptic plasticity can simulate the "learning-forgetting-erase" and "instant display" processes. ZnO NWs may enable the development of neuromorphic computers that can use the same material to achieve both short-term computation and long-term memory.

Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks.

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

Effect of systematic nursing intervention on rehabilitation after colorectal polyps of endoscopic removal in children: A protocol for systematic review and meta-analysis.

BACKGROUND: Endoscopic removal is the main method for the treatment of colorectal polyps in children. Due to the small age of children, poor coordination, postoperative sensitive postoperative response, it is not good for postoperative recovery. Systematic nursing has an advantage in promoting the postoperative recovery of children with colorectal polyps of endoscopic removal, but it is lack of evidence-based basis. The purpose of this study is to evaluate the effect of systematic nursing intervention on the rehabilitation of children after colorectal polyps of endoscopic removal. METHODS: China National Knowledge Infrastructure, Wanfang, China Science and Technology Journal Database and Chinese Biomedical Literature Database, PubMed, Embase, Web of Science, and the Cochrane Library databases will be searched by computer. A randomized controlled study is searched on the application of systematic nursing intervention of children with colorectal polyps of endoscopic removal from the establishment of the database in February 2021. The language is limited to English and Chinese. The quality of the included study is independently extracted and the literature quality is evaluated by 2 researchers. The RevMan5.3 software is used to conduct meta-analysis of the included literature. RESULTS: This study will evaluate the effect of systematic nursing intervention on the rehabilitation of children after colorectal polyps of endoscopic removal by the indexes of total effective rate, complication rate, and hospital stays. CONCLUSION: This study will provide reliable evidence-based basis for establishing a reasonable and effective nursing intervention for endoscopic removal of colorectal polyps in childhood. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/S57UX.

Ultrafast Detection and Discrimination of Methanol Gas Using a Polyindole-Embedded Substrate Integrated Waveguide Microwave Sensor.

The fast and sensitive detection of methanol gas using cost-effective sensors in the industry is a significant issue to be addressed. Herein, a polyindole (PIn)-deposited substrate integrated waveguide (SIW) has been introduced to perform quantitative and qualitative methanol gas sensing with quick response and recovery time at room temperature. First, PIn is synthesized and deposited in the microwell etched at the intensified electric field region of the microwave-based cavity resonator, which gives a sensing response through variation of PIn's high-frequency conductivity and dielectric property caused by the adsorption and desorption of methanol gas. Second, an enhanced filling factor and high Q factor have been attained using the proposed microwell etched SIW structure, which exhibits high sensitivity in terms of frequency shift (3.33 kHz/ppm), amplitude shift (0.005 dB/ppm), bandwidth broadening (3.66 kHz/ppm), and loaded Q factor (10.60 Q value/ppm). Third, the gas measurement results reveal excellent long-term stability with a relative standard deviation (RSD) of 0.02% for 7 days, excellent repeatability with an RSD of 0.004%, and desired response and recovery time of 95 and 120 s, respectively. The results indicate that the proposed microwave sensor has great potential to achieve high sensitivity and fast response toward methanol gas molecules at room temperature.

Development of Fully Flexible Tactile Pressure Sensor with Bilayer Interlaced Bumps for Robotic Grasping Applications.

Flexible tactile sensors have been utilized in intelligent robotics for human-machine interaction and healthcare monitoring. The relatively low flexibility, unbalanced sensitivity and sensing range of the tactile sensors are hindering the accurate tactile information perception during robotic hand grasping of different objects. This paper developed a fully flexible tactile pressure sensor, using the flexible graphene and silver composites as the sensing element and stretchable electrodes, respectively. As for the structural design of the tactile sensor, the proposed bilayer interlaced bumps can be used to convert external pressure into the stretching of graphene composites. The fabricated tactile sensor exhibits a high sensing performance, including relatively high sensitivity (up to 3.40% kPa-1), wide sensing range (200 kPa), good dynamic response, and considerable repeatability. Then, the tactile sensor has been integrated with the robotic hand finger, and the grasping results have indicated the capability of using the tactile sensor to detect the distributed pressure during grasping applications. The grasping motions, properties of the objects can be further analyzed through the acquired tactile information in time and spatial domains, demonstrating the potential applications of the tactile sensor in intelligent robotics and human-machine interfaces.

A multifunctional skin-like wearable optical sensor based on an optical micro-/nanofibre.

Multifunctional skin-like sensors play an important role in next-generation healthcare, robotics, and bioelectronics. Here, we report a skin-like wearable optical sensor (SLWOS) enabled by a stretchable, flexible, and attachable patch embedded with an optical micro-/nanofibre (MNF), which is highly compatible with human skin, a curved surface, or cloth. Based on the transition from radiation modes into guided modes around the bending area of the MNF, the SLWOS embedded with a wavy MNF is highly sensitive to weak strain, achieving a gauge factor as large as 675 (strain <1%). The flexible SLWOS is also capable of monitoring the bending angle in a broad dynamic range with tunable sensitivity. In addition, temperature measurements in the range of -20 to 130 °C are realized by taking advantage of PDMS's large negative thermo-optic coefficient. The superior sensing performance together with mechanical flexibility enables the real-time monitoring of respiration, arm motion, and body temperature. This SLWOS will have great potential in wearable optical devices ranging from ultrasensitive sensors to photonic healthcare devices.

Fully Elastomeric Fingerprint-Shaped Electronic Skin Based on Tunable Patterned Graphene/Silver Nanocomposites.

Multifunctional electronic skins (e-skins), which mimic the somatosensory system of human skin, have been widely employed in wearable devices for intelligent robotics, prosthetics, and human health monitoring. Relatively low sensitivity and severe mutual interferences of multiple stimuli detection have limited the applications of the existing e-skins. To address these challenges, inspired by the physical texture of the natural fingerprint, a novel fully elastomeric e-skin is developed herein for highly sensitive pressure and temperature sensing. A region-partition strategy is utilized to construct the multifunctional fingerprint-shaped sensing elements, where strain isolation structure of indurated film patterns are further embedded to enhance the sensitivity and effectively reduce mutual interferences between the differentiated units. The fully elastomeric graphene/silver/silicone rubber nanocomposites are synthesized with tunable properties including conductivity and sensitivity to satisfy the requirements of highly sensitive pressure and temperature sensing as well as stretchable electrodes. Remarkable progress in sensitivities for both pressure and temperature, up to 5.53 kPa-1 in a wide range of 0.5-120 kPa and 0.42% °C-1 in 25-60 °C, respectively, are achieved with the inappreciable mutual interferences. Further studies demonstrate the great potential of the proposed e-skin in the next-generation of wearable electronics for human-machine interfaces.

Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks.

Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme.

Effect of Ba and Pb dual doping on the thermoelectric properties of BiCuSeO ceramics.

Besides the thermoelectric properties, mechanically robust is also very important for applications in TEGs. Up to now, no studies have been reported to investigate the mechanical properties of BiCuSeO oxyselenides. In this work, the results of hardness test of pristine and Ba/Pb doped BiCuSeO are presented here. These data may help to further evaluate the mechanical properties of BiCuSeO based ceramics.

Magnetic-Field-Assisted Assembly of Anisotropic Superstructures by Iron Oxide Nanoparticles and Their Enhanced Magnetism.

Magnetic nanoparticle superstructures with controlled magnetic alignment and desired structural anisotropy hold promise for applications in data storage and energy storage. Assembly of monodisperse magnetic nanoparticles under a magnetic field could lead to highly ordered superstructures, providing distinctive magnetic properties. In this work, a low-cost fabrication technique was demonstrated to assemble sub-20-nm iron oxide nanoparticles into crystalline superstructures under an in-plane magnetic field. The gradient of the applied magnetic field contributes to the anisotropic formation of micron-sized superstructures. The magnitude of the applied magnetic field promotes the alignment of magnetic moments of the nanoparticles. The strong dipole-dipole interactions between the neighboring nanoparticles lead to a close-packed pattern as an energetically favorable configuration. Rod-shaped and spindle-shaped superstructures with uniform size and controlled spacing were obtained using spherical and polyhedral nanoparticles, respectively. The arrangement and alignment of the superstructures can be tuned by changing the experimental conditions. The two types of superstructures both show enhancement of coercivity and saturation magnetization along the applied field direction, which is presumably associated with the magnetic anisotropy and magnetic dipole interactions of the constituent nanoparticles and the increased shape anisotropy of the superstructures. Our results show that the magnetic-field-assisted assembly technique could be used for fabricating nanomaterial-based structures with controlled geometric dimensions and enhanced magnetic properties for magnetic and energy storage applications.