A Novel Artificial Neuron-Like Gas Sensor Constructed from CuS Quantum Dots/Bi2S3 Nanosheets

Free carrier-enhanced Bi/Bi2S3 nanoparticles enable precise OCT catheter-guided interventional photothermal therapy for colorectal cancer

Current clinical colorectal cancer treatments usually possess unsatisfactory effects, mainly because of unavoidable surgical trauma and multidrug resistance. Precise and minimally invasive theragnostic technology has advanced through miniaturized catheter intervention with imaging-guided treatment methods; however, previously reported approaches cannot simultaneously perform in situ real-time imaging and therapy. We proposed a strategy of 0.9 mm catheter-based optical coherence tomography imaging-guided interventional photothermal therapy at 1310 nm for orthotopic colorectal cancer. Specifically, folate-modified Bi/Bi2S3 nanoparticles showed intense scattering signals and local hyperpyrexia under 1310 nm laser irradiation in vitro and in vivo due to the localized surface plasmon resonance effect, enabling imaging-guided precise tumor treatment. Histopathological and blood biochemistry analyses confirmed the high biosafety and negligible long-term toxicity of Bi/Bi2S3 nanoparticles. This new method offers a feasible methodology for catheter-based precise interventional photon theragnostics. STATEMENT OF SIGNIFICANCE: Emerging minimally invasive techniques have been explored for the treatment of colorectal cancer (CRC); however, these reported approaches cannot reach the requirement of precise orthotopic cancer treatment due to the lack of in situ real-time imaging guidance. This study proposes a 0.9 mm catheter-based OCT imaging-guided interventional photothermal therapy (IPTT) strategy at 1310 nm for treating orthotopic CRC. Folate-modified plasmonic Bi/Bi2S3 nanoparticles enable real-time imaging-guided IPTT by providing strong scattering signals and local hyperthermia. This approach allows simultaneous transmission of imaging and therapy light in the same optical fiber, offering a promising method for precise CRC theragnostics and addressing the gap of in situ real-time imaging during IPTT.

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications

As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept and structure of memristor devices are introduced. The selection of functional materials for low-power memristors is discussed, including ion transport materials, phase change materials, magnetoresistive materials, and ferroelectric materials. Two common types of memristor arrays, 1T1R and 1S1R crossbar arrays are introduced, and physical diagrams of edge computing memristor chips are discussed in detail. Potential applications of low-power memristors in advanced multi-value storage, digital logic gates, and analogue neuromorphic computing are summarized. Furthermore, the future challenges and outlook of neuromorphic computing based on memristor are deeply discussed.

Tunable Synthesis of 2D Bismuth Oxyhydroxide and Oxysulfide from Solid-Liquid Interfacial Reaction for High Performance Optoelectronic Gas Sensing

The self-limiting Cabrera-Mott oxidation reaction on metal surfaces provides an effective pathway for synthesizing atomically thin 2D metal oxide. Inspired by this reaction, it is proposed that solid bismuth metals can react with dissolved oxygen and water molecules in an aqueous environment, forming an ultrathin oxyhydroxide layer on their surfaces. The lattice mismatch between the surface oxyhydroxide layer and the underlying pure metal enables the mechanical exfoliation of detached 2D oxyhydroxide nanosheets. Moreover, the sulfurization interaction between the oxyhydroxide and dissolved H2S expands the applicability of solid-liquid interfacial reactions for realizing 2D bismuth oxysulfide, effectively tuning their electronic bandgap energy, work function, and band position. Given its good photoresponse from blue to UV light, the optoelectronic gas sensing performances of bismuth oxysulfide are investigated. Under purple light irradiation, the Bi2O1.12S1.88-based gas sensor exhibits an excellent optoelectronic response factor of 48.5% toward 10 ppb NO2, which is the lowest detection limit for reported bismuth compounds-based gas sensor so far. This work provides a novel and facile synthesis approach for 2D metal oxyhydroxide and oxysulfide and simultaneously demonstrates the substantial potential of bismuth oxysulfide in high-performance optoelectronic gas sensing.

Advances in 0D quantum dots and hybrid nanoarchitectures for high-performance gas sensing devices

Zero-dimensional quantum dots (QDs) and their hybrid structures having been rapidly developed are reshaping the design and performance of next generation ultrafast electronic and optoelectronic devices. The high-performance metrics achievable in photodetectors, solar cells, transistors, and other application areas can be realized through the use of QDs with their tunable electronic and optical properties. Recent advances in the synthesis of QD hybrid structures, where QDs are incorporated within other nanostructure dimensions (1D nanowires, 2D materials), have dramatically increased charge carrier mobility, lowered recombination rates, and resulted in highly controlled interfacial properties. Synergistic effects between these hybrid configurations are exploited, including improved charge separation and enhanced exciton dissociation, which are very important for having ultrafast response times and greater sensitivity. Advanced fabrication techniques such as chemical vapor deposition and solution based self-assembly, QD hybrids can be fabricated with highly controlled interfaces and optimal energy band alignments. Further, computational simulations such as density functional theory (DFT) and time dependent DFT have provided further insights into the charge dynamics and electronic interactions in these hybrid systems for guidance on their design and application. The potential of QD-based hybrid architectures in addressing future information processing demands is demonstrated in this work, setting the stage for the development of high-speed, low-power devices in communications, sensing, and renewable energy technologies.

Enhancing NO2 Gas Sensing: The Dual Impact of UV and Thermal Activation on Vertically Aligned Nb-MoS2 for Superior Response and Selectivity

Nitrogen dioxide (NO2) is considered to be a highly hazardous gas found in combustion engine exhaust, which causes several diseases at a young age. To detect NO2 at room temperature (RT), two-dimensional transition metal dichalcogenides play an essential role because of their greater surface-to-volume ratio. However, their higher limit of detection (LOD), slow response, and incomplete recovery kinetics hinder their use in efficient gas sensors. To mitigate these issues, we fabricate a facile and robust niobium (Nb)-doped molybdenum disulfide (MoS2) sensor using low-pressure chemical vapor deposition on a SiO2/Si substrate. Doping is confirmed through various characterization techniques. As compared to pristine MoS2, three batches of sensors are prepared with different weight percentages of Nb (8, 16, and 24%). Out of these, the 16% Nb-MoS2 sensor gives a greatly enhanced relative response of ∼30% for 500 ppb NO2 at 100 °C with an LOD of 489 ppt. Also, the sensor gives an ultrahigh response of ∼39% (18%) for 50 ppm (500 ppb) NO2 under 0.4 mW/cm2 intensity of UV light and exhibits a lower LOD of 117 ppt at RT. In addition, the 16% Nb-MoS2 sensor shows impressive selectivity toward NO2 against a range of reducing and oxidizing gases, along with exceptional long-term durability and stability. Based on density functional theory calculations, a comprehensive gas sensing mechanism is proposed. The calculations focus on identifying the favorable sites for NO2 adsorption on 16% Nb-MoS2 nanoflakes. This study offers a compelling and practical approach to boosting the efficiency of Nb-MoS2-based NO2 gas sensors.

Bioinspired Electrolyte-Gated Organic Synaptic Transistors: From Fundamental Requirements to Applications

Rapid development of artificial intelligence requires the implementation of hardware systems with bioinspired parallel information processing and presentation and energy efficiency. Electrolyte-gated organic transistors (EGOTs) offer significant advantages as neuromorphic devices due to their ultra-low operation voltages, minimal hardwired connectivity, and similar operation environment as electrophysiology. Meanwhile, ionic-electronic coupling and the relatively low elastic moduli of organic channel materials make EGOTs suitable for interfacing with biology. This review presents an overview of the device architectures based on organic electrochemical transistors and organic field-effect transistors. Furthermore, we review the requirements of low energy consumption and tunable synaptic plasticity of EGOTs in emulating biological synapses and how they are affected by the organic materials, electrolyte, architecture, and operation mechanism. In addition, we summarize the basic operation principle of biological sensory systems and the recent progress of EGOTs as a building block in artificial systems. Finally, the current challenges and future development of the organic neuromorphic devices are discussed.

Photonic Chip Based on Ultrafast Laser-Induced Reversible Phase Change for Convolutional Neural Network

Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence. Due to the advantages in computing speed, integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm. Programmable photonic chips are vital for achieving practical applications of photonic computing. Herein, a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing. Through designing the ultrafast laser pulses, the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase, resulting in a large contrast in refractive index and extinction coefficient. As a consequence, the light transmission of waveguides can be switched between write and erase states. To determine the phase change time, the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale, and the time-resolved transient reflectivity is measured. Based on the integrated photonic chip, photonic convolutional neural networks are built to implement machine learning algorithm, and images recognition task is achieved. This work paves a route for fabricating programmable photonic chips by designed ultrafast laser, which will facilitate the application of photonic computing in artificial intelligence.

Transferrin-Based Bismuth Nanoparticles for Radiotherapy with Immunomodulation Against Orthotopic Glioma

Modern radiotherapy frequently employs radiosensitizers for radiation dose deposition and triggers an immunomodulatory effect to enhance tumor destruction. However, developing glioma-targeted sensitizers remains challenging due to the blood-brain barrier (BBB) and multicomponent instability. This study aims to green-synthesize transferrin-bismuth nanoparticles (TBNPs) as biosafe radiosensitizers to enhance X-ray absorption by tumors and stimulate the immune response for glioma therapy. The proposed protein-based strategy provides TBNPs with BBB-crossing ability and prevents off-target toxicity. Cellular experiments following 4 Gy of X-ray irradiation reveal that TBNPs increase DNA damage in glioma cells and trigger immunomodulation, thereby inducing immunogenic cell death. Furthermore, TBNPs effectively inhibit tumor growth through synergistic radiotherapy and immunotherapy in an orthotopic glioma mouse model. The findings highlight TBNPs as promising radiosensitizers for effective and biosafe radiotherapy with immunomodulation.

Wearable Biodevices Based on Two-Dimensional Materials: From Flexible Sensors to Smart Integrated Systems

The proliferation of wearable biodevices has boosted the development of soft, innovative, and multifunctional materials for human health monitoring. The integration of wearable sensors with intelligent systems is an overwhelming tendency, providing powerful tools for remote health monitoring and personal health management. Among many candidates, two-dimensional (2D) materials stand out due to several exotic mechanical, electrical, optical, and chemical properties that can be efficiently integrated into atomic-thin films. While previous reviews on 2D materials for biodevices primarily focus on conventional configurations and materials like graphene, the rapid development of new 2D materials with exotic properties has opened up novel applications, particularly in smart interaction and integrated functionalities. This review aims to consolidate recent progress, highlight the unique advantages of 2D materials, and guide future research by discussing existing challenges and opportunities in applying 2D materials for smart wearable biodevices. We begin with an in-depth analysis of the advantages, sensing mechanisms, and potential applications of 2D materials in wearable biodevice fabrication. Following this, we systematically discuss state-of-the-art biodevices based on 2D materials for monitoring various physiological signals within the human body. Special attention is given to showcasing the integration of multi-functionality in 2D smart devices, mainly including self-power supply, integrated diagnosis/treatment, and human-machine interaction. Finally, the review concludes with a concise summary of existing challenges and prospective solutions concerning the utilization of 2D materials for advanced biodevices.

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.

Recent Advances in Engineering of 2D Layered Metal Chalcogenides for Resistive-Type Gas Sensor

2D nanomaterials have triggered widespread attention in sensing applications. Especially for 2D layered metal chalcogenides (LMCs), the unique semiconducting properties and high surface area endow them with great potential for gas sensors. The assembly of 2D LMCs with guest species is an effective functionalization method to produce the synergistic effects of hybridization for greatly enhancing the gas-sensing properties. This review starts with the synthetic techniques, sensing properties, and principles, and then comprehensively compiles the advanced achievements of the pristine 2D LMCs gas sensors. Key advances in the development of the functionalization of 2D LMCs for enhancing gas-sensing properties are categorized according to the spatial architectures. It is systematically discussed in three aspects: surface, lattice, and interlayer, to comprehend the benefits of the functionalized 2D LMCs from surface chemical effect, electronic properties, and structure features. The challenges and outlooks for developing high-performance 2D LMCs-based gas sensors are also proposed.

A Multifunctional Hydrogel with Multimodal Self-Powered Sensing Capability and Stable Direct Current Output for Outdoor Plant Monitoring Systems

Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges. These systems facilitate informed decisions that enhance efficiency in broader environmental management. Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources, low durability under harsh conditions, non-transparent or non-biocompatible materials, and complex structures. Herein, a multifunctional hydrogel is developed, which can fulfill all the above requirements and build self-sustainable outdoor monitoring systems solely by it. It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m-3 for nearly 60 days of operation in normal environments (24 °C, 60% RH), with an energy density of around 1.36 × 107 J m-3. It also shows good self-recoverability in severe environments (45 °C, 30% RH) in nearly 40 days of continuous operation. Moreover, this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content, providing critical data on evaluating plant health, previously obtainable only through invasive or high-power consumption methods. Its potential extends to acting as other self-powered environmental sensors. This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production, paving the way for future agriculture.

Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges

Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

Quantum Confinement and End-Sealing Effects for Highly Sensitive and Stable Nitrogen Dioxide Detection: Homogeneous Integration of Ti3C2Tx-Based Flexible Gas Sensors

The real-time and room-temperature detection of nitrogen dioxide (NO2) holds significant importance for environmental monitoring. However, the performance of NO2 sensors has been hampered by the trade-off between the high sensitivity and stability of conventional sensitive materials. Here, we present a novel fully flexible paper-based gas sensing structure by combining a homogeneous screen-printed titanium carbide (Ti3C2Tx) MXene-based nonmetallic electrode with a MoS2 quantum dots/Ti3C2Tx (MoS2 QDs/Ti3C2Tx) gas-sensing film. These precisely designed gas sensors demonstrate an improved response value (16.3% at 5 ppm) and a low theoretical detection limit of 12.1 ppb toward NO2, which exhibit a remarkable 3.5-fold increase in sensitivity compared to conventional Au interdigital electrodes. The outstanding performance can be attributed to the integration of the quantum confinement effect of MoS2 QDs and the conductivity of Ti3C2Tx, establishing the main active adsorption sites and enhanced charge transport pathways. Furthermore, an end-sealing effect strategy was applied to decorate the defect sites with naturally oxygen-rich tannic acid and conductive polymer, and the formed hydrogen bonding network at the interface effectively mitigated the oxidative degradation of the Ti3C2Tx-based gas sensors. The exceptional stability has been achieved with only a 1.8% decrease in response over 4 weeks. This work highlights the innovative design of high-performance gas sensing materials and homogeneous gas sensor techniques.

Unraveling the Effect of Oxygen Vacancy on WO3 Surface for Efficient NO2 Detection at Low Temperature

Oxygen vacancies (VO) in metal oxide semiconductors play an important role in improving gas-sensing performance of chemiresistive gas sensors. Nonetheless, there is still a lack of clear understanding of the inherent mechanism of the influence of oxygen vacancies on gas sensing due to generally focusing on the concentration of VO. Herein, oxygen vacancies were rationally modulated in WO3 nanoflower structures via an annealing process, resulting in a transformation of VO from neutral (VO0) to a doubly ionized (VO2+) state. Density functional theory (DFT) calculations indicate that VO2+ is significantly more efficient than VO0 for NO2 detection in competition with atmospheric O2. Benefiting from a high concentration of VO2+, the WO3-450 (WO3 annealed at 450 °C) sensor exhibits excellent sensing performance with an ultrahigh sensitivity (3674.1 to 5 ppm NO2), superior selectivity, and long-term stability (one month). Furthermore, the sensor with the wide range of concentration detection not only can detect NO2 gas with parts per million (ppm) but also can detect NO2 with parts per billion (ppb) level concentration, with a high sensibility reaching 2.8 to 25 ppb NO2 and over 100 to 100 ppb NO2. This study elucidates the oxygen vacancy mediated sensing mechanism toward NO2 and provides an effective strategy for the rational design of gas sensors with high sensing performance.

Enhanced oxygen anions generation on Bi2S3/Sb2S3 heterostructure by visible light for trace H2S detection at room temperature

Bismuth sulfide (Bi2S3) possesses unique properties that make it a promising material for effective hydrogen sulfide (H2S) detection at room temperature. However, when exposed to light, the oxygen anions (O2-(ads)) adsorbed on the surface of Bi2S3 can react with photoinduced holes, ultimately reducing the ability to respond to H2S. In this study, Bi2S3/Sb2S3 heterostructures were synthesized, producing photoinduced oxygen anions (O2-(hv)) under visible light conditions, resulting in enhanced H2S sensing capability. The Bi2S3/Sb2S3 heterostructure sensor exhibits a two-fold increase in sensing response to 500 ppb H2S under in door light conditions relative to its performance in darkness. Additionally, the sensing response of the Bi2S3/Sb2S3 sensor (Ra/Rg= 23.3) was approximately five times higher than pure Bi2S3. The improved sensing performance of the Bi2S3/Sb2S3 heterostructures is attributable to the synergistic influence of the heterostructure configuration and light modulation, which enhances the H2S sensing performance by facilitating rapid charge transfer and increasing active sites (O2-(hv)) when exposed to visible light.

Heterogeneous CuS QDs/BiVO4@Y2O2S Nanoreactor for Monitorable Photocatalysis

Exploration of multifunctional integrated catalysts is of great significance for photocatalysis toward practical application. Herein, a 1D confined nanoreactor with a heterogeneous core-shell structure is designed for synergies of efficient catalysis and temperature monitoring by custom encapsulation of Z-scheme heterojunction CuS quantum dots/BiVO4 (CuS QDs/BiVO4) and Y2O2S-Er, Yb. The dispersed active sites created by the QDs with high surface energy improve the mass transfer efficiency, and the efficient electron transport channels at the heterogeneous interface extend the carrier lifetime, which endows the nanoreactor with excellent catalytic performance. Meanwhile, real-time temperature monitoring is realized based on the thermally coupled levels 2H11/2/4S3/2→4I15/2 of Er3+ using fluorescence intensity ratio, which enables the monitorable photocatalysis. Furthermore, the nanoreactor with a multidimensional structure increases effective intermolecular collisions to facilitate the catalytic process by restricting the reaction within distinct enclosed spaces and circumvents potential unknown interaction effects. The design of multi-space nanoconfined reactors opens up a new avenue to modulate catalyst function, providing a unique perspective for photocatalytic applications in the mineralization of organic pollutants, hydrogen production, and nitrogen fixation.

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Advances on chalcogenide quantum dots-based sensors for environmental pollutants monitoring

Water contamination represents a significant ecological impact with global consequences, contributing to water scarcity worldwide. The presence of several pollutants, including heavy metals, pharmaceuticals, pesticides, and pathogens, in water resources underscores a pressing global concern, prompting the European Union (EU) to establish a Water Watch List to monitor the level of these substances. Nowadays, the standard methods used to detect and quantify these contaminants are mainly liquid or gas chromatography coupled with mass spectrometry (LC/GC-MS). While these methodologies offer precision and accuracy, they require expensive equipment and experienced technicians, and cannot be used on the field. In this context, chalcogenide quantum dots (QDs)-based sensors have emerged as promising, user-friendly, practical, and portable tools for environmental monitoring. QDs are semiconductor nanocrystals that possess excellent properties, and have demonstrated versatility across various sensor types, such as fluorescent, electrochemical, plasmonic, and colorimetric ones. This review summarizes recent advances (2019-2023) in the use of chalcogenide QDs for environmental sensing, highlighting the development of sensors capable of detect efficiently heavy metals, anions, pharmaceuticals, pesticides, endocrine disrupting compounds, organic dyes, toxic gases, nitroaromatics, and pathogens.

Advanced Design of Soft Robots with Artificial Intelligence

A comprehensive review focused on the whole systems of the soft robotics with artificial intelligence, which can feel, think, react and interact with humans, is presented. The design strategies concerning about various aspects of the soft robotics, like component materials, device structures, prepared technologies, integrated method, and potential applications, are summarized. A broad outlook on the future considerations for the soft robots is proposed.

In recent years, breakthrough has been made in the field of artificial intelligence (AI), which has also revolutionized the industry of robotics. Soft robots featured with high-level safety, less weight, lower power consumption have always been one of the research hotspots. Recently, multifunctional sensors for perception of soft robotics have been rapidly developed, while more algorithms and models of machine learning with high accuracy have been optimized and proposed. Designs of soft robots with AI have also been advanced ranging from multimodal sensing, human–machine interaction to effective actuation in robotic systems. Nonetheless, comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare. Here, the new development is systematically reviewed in the field of soft robots with AI. First, background and mechanisms of soft robotic systems are briefed, after which development focused on how to endow the soft robots with AI, including the aspects of feeling, thought and reaction, is illustrated. Next, applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement. Design thoughts for future intelligent soft robotics are pointed out. Finally, some perspectives are put forward.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artificial intelligence. However, great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years. Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation, improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions. Herein, several fascinating strategies for synaptic plasticity modulation through chemical techniques, device structure design, and physical signal sensing are reviewed. For chemical techniques, the underlying mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted. Based on device structure design, the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions. Besides, integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light, strain, and temperature. Finally, considering that the relevant technology is still in the basic exploration stage, some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

Ultra-Stable High-Capacity Polythiophene Derivative for Wide-Potential-Window Supercapacitors

Conducting polymer (CP)-based supercapacitors show great promise for applications in the field of wearable and portable electronics. However, these supercapacitors face persistent challenges, notably low energy density and inadequate stability. In this study, we introduce a polythiophene derivative, designated as poly(EPE), synthesized via the electrochemical polymerization of 8-bis(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-3,3-dimethyl-3,4-dihydro-2H-thieno[3,4-b][1,4]dioxepine (EPE). The resulting poly(EPE) polymer exhibits an exemplary 3D porous network-like structure, significantly enhancing its capacitance performance. When employed as the electrode material, the symmetric supercapacitor demonstrates an exceptionally high specific capacitance of 1342 F g-1 at a current density of 4.0 A g-1, along with impressive energy and power densities of 119.3 W h kg-1 and 38.83 kW kg-1, respectively. These capacitance values surpass those of previously reported pristine CP-based supercapacitors. Notably, the supercapacitor showcases outstanding stability, maintaining a retention rate of 92.5% even after 50,000 charge-discharge cycles. These findings underscore the substantial potential of poly(EPE) as an electrode material for the advancement of the supercapacitor technology.

Nitrogen-Based Gas Molecule Adsorption on a ReSe2 Monolayer via Single-Atom Doping: A First-Principles Study

Two-dimensional materials have shown immense promise for gas-sensing applications due to their remarkable surface-to-volume ratios and tunable chemical properties. However, despite their potential, the utilization of ReSe2 as a gas-sensing material for nitrogen-containing molecules, including NO2, NO, and NH3, has remained unexplored. The choice of doping atoms in ReSe2 plays a pivotal role in enhancing the gas adsorption and gas-sensing capabilities. Herein, the adsorption properties of nitrogen-containing gas molecules on metal and non-metal single-atom (Au, Pt, Ni, P, and S)-doped ReSe2 monolayers have been evaluated systematically via ab initio calculations based on density functional theory. The findings strongly suggest that intrinsic ReSe2 has better selectivity toward NO2 than toward NO and NH3. Moreover, our results provide compelling evidence that all of the dopants, with the exception of S, significantly enhance both the adsorption strength and charge transfer between ReSe2 and the investigated molecules. Notably, P-decorated ReSe2 showed the highest adsorption energy for NO2 and NO (-1.93 and -1.52 eV, respectively) with charge transfer above 0.5e, while Ni-decorated ReSe2 exhibited the highest adsorption energy for NH3 (-0.76 eV). In addition, on the basis of transition theory, we found that only Au-ReSe2 and Ni-ReSe2 can serve as reusable chemiresisitve gas sensors for reliable detection of NO and NH3, respectively. Hence, our findings indicate that gas-sensing applications can be significantly improved by utilizing a single-atom-doped ReSe2 monolayer.

Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials

The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.

Laser-Induced and MOF-Derived Metal Oxide/Carbon Composite for Synergistically Improved Ethanol Sensing at Room temperature

Advancements in sensor technology have significantly enhanced atmospheric monitoring. Notably, metal oxide and carbon (MOx/C) hybrids have gained attention for their exceptional sensitivity and room-temperature sensing performance. However, previous methods of synthesizing MOx/C composites suffer from problems, including inhomogeneity, aggregation, and challenges in micropatterning. Herein, we introduce a refined method that employs a metal-organic framework (MOF) as a precursor combined with direct laser writing. The inherent structure of MOFs ensures a uniform distribution of metal ions and organic linkers, yielding homogeneous MOx/C structures. The laser processing facilitates precise micropatterning (< 2 μm, comparable to typical photolithography) of the MOx/C crystals. The optimized MOF-derived MOx/C sensor rapidly detected ethanol gas even at room temperature (105 and 18 s for response and recovery, respectively), with a broad range of sensing performance from 170 to 3,400 ppm and a high response value of up to 3,500%. Additionally, this sensor exhibited enhanced stability and thermal resilience compared to previous MOF-based counterparts. This research opens up promising avenues for practical applications in MOF-derived sensing devices.

Optoelectronic Synapses Based on MXene/Violet Phosphorus van der Waals Heterojunctions for Visual-Olfactory Crossmodal Perception

The crossmodal interaction of different senses, which is an important basis for learning and memory in the human brain, is highly desired to be mimicked at the device level for developing neuromorphic crossmodal perception, but related researches are scarce. Here, we demonstrate an optoelectronic synapse for vision-olfactory crossmodal perception based on MXene/violet phosphorus (VP) van der Waals heterojunctions. Benefiting from the efficient separation and transport of photogenerated carriers facilitated by conductive MXene, the photoelectric responsivity of VP is dramatically enhanced by 7 orders of magnitude, reaching up to 7.7 A W-1. Excited by ultraviolet light, multiple synaptic functions, including excitatory postsynaptic currents, paired-pulse facilitation, short/long-term plasticity and "learning-experience" behavior, were demonstrated with a low power consumption. Furthermore, the proposed optoelectronic synapse exhibits distinct synaptic behaviors in different gas environments, enabling it to simulate the interaction of visual and olfactory information for crossmodal perception. This work demonstrates the great potential of VP in optoelectronics and provides a promising platform for applications such as virtual reality and neurorobotics.

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.

Outstanding Humidity Chemiresistors Based on Imine-Linked Covalent Organic Framework Films for Human Respiration Monitoring

Human metabolite moisture detection is important in health monitoring and non-invasive diagnosis. However, ultra-sensitive quantitative extraction of respiration information in real-time remains a great challenge. Herein, chemiresistors based on imine-linked covalent organic framework (COF) films with dual-active sites are fabricated to address this issue, which demonstrates an amplified humidity-sensing signal performance. By regulation of monomers and functional groups, these COF films can be pre-engineered to achieve high response, wide detection range, fast response, and recovery time. Under the condition of relative humidity ranging from 13 to 98%, the COFTAPB-DHTA film-based humidity sensor exhibits outstanding humidity sensing performance with an expanded response value of 390 times. Furthermore, the response values of the COF film-based sensor are highly linear to the relative humidity in the range below 60%, reflecting a quantitative sensing mechanism at the molecular level. Based on the dual-site adsorption of the (-C=N-) and (C-N) stretching vibrations, the reversible tautomerism induced by hydrogen bonding with water molecules is demonstrated to be the main intrinsic mechanism for this effective humidity detection. In addition, the synthesized COF films can be further exploited to effectively detect human nasal and oral breathing as well as fabric permeability, which will inspire novel designs for effective humidity-detection devices.

Electric Resistance of Elastic Strain Sensors-Fundamental Mechanisms and Experimental Validation

Elastic strain sensor nanocomposites are emerging materials of high scientific and commercial interest. This study analyzes the major factors influencing the electrical behavior of elastic strain sensor nanocomposites. The sensor mechanisms were described for nanocomposites with conductive nanofillers, either dispersed inside the polymer matrix or coated onto the polymer surface. The purely geometrical contributions to the change in resistance were also assessed. The theoretical predictions indicated that maximum Gauge values are achieved for mixture composites with filler fractions slightly above the electrical percolation threshold, especially for nanocomposites with a very rapid conductivity increase around the threshold. PDMS/CB and PDMS/CNT mixture nanocomposites with 0-5.5 vol.% fillers were therefore manufactured and analyzed with resistivity measurements. In agreement with the predictions, the PDMS/CB with 2.0 vol.% CB gave very high Gauge values of around 20,000. The findings in this study will thus facilitate the development of highly optimized conductive polymer composites for strain sensor applications.

Multifunctional Optoelectronic Synapses Based on Arrayed MoS2 Monolayers Emulating Human Association Memory

Optoelectronic synaptic devices integrating light-perception and signal-storage functions hold great potential in neuromorphic computing for visual information processing, as well as complex brain-like learning, memorizing, and reasoning. Herein, the successful growth of MoS₂ monolayer arrays assisted by gold nanorods guided precursor nucleation is demonstrated. Optical, spectral, and morphology characterizations of MoS₂ prove that arrayed flakes are homogeneous monolayers, and they are further fabricated as optoelectronic devices showing featured photocurrent loops and stable optical responses. Typical synaptic behaviors of photo-induced short-term potentiation, long-term potentiation, and paired pulse facilitation are recorded under different light stimulations of 450, 532, and 633 nm lasers at various excitation powers. A visual sensing system consisting of 5 × 6 pixels is constructed to simulate the light-sensing image mapped by forgetting curves in real time. Moreover, the system presents the ability of utilizing associated images to restore vague and incomplete memories, which successfully mimics human intelligent behaviors of association memory and logical reasoning. The work emulates the brain-like artificial intelligence using arrayed 2D semiconductors, which paves an avenue to achieve smart retina and complex brain-like system.

Synergy of S-vacancy and heterostructure in BiOCl/Bi2S3-x boosting room-temperature NO2 sensing

The special physicochemical properties of Bi₂S₃ nanomaterial endow it to be exceptional NO₂ sensing properties. However, sensors based on pure Bi₂S₃ cannot detect trace NO₂ at room temperature effectively due to the scanty active sites and poor charge transfer efficiency. Herein, vacancy defect and heterostructure engineering are rationally integrated to explore BiOCl/Bi₂S_3-x heterostructure with rich S vacancies to enhance NO₂ sensing performance. The optimized sensor based on S-vacancy-rich BiOCl/Bi₂S_3-x heterostructure exhibited a high response value (R_g/R_a = 29.1) to 1 ppm NO₂ at room temperature, which was about 17 times compared to the pristine Bi₂S₃. Meanwhile, the BiOCl/Bi₂S_3-x sensor also exhibited a short response time (36 s) towards 1 ppm NO₂ and a low theoretical detection limit (2 ppb). The superior response value of S-vacancy-rich BiOCl/Bi₂S_3-x heterostructures was ascribed to the improved electron migration at the heterointerface and the additional exposed active sites caused by the S vacancies in Bi₂S_3-x. Additionally, the sensors based on S-vacancy-rich BiOCl/Bi₂S_3-x heterostructures showed good long-term stability, outstanding selectivity, and good flexibility. This study offers an effective method for synergistically engineering defect and heterostructure to enhance gas sensing properties at room temperature.

Fully Flexible MXene-based Gas Sensor on Paper for Highly Sensitive Room-Temperature Nitrogen Dioxide Detection

Flexible chemiresistive gas sensors have attracted growing interest due to their capability in real-time and rapid detection of gas. However, the performance of gas sensors has long been hindered by the poor charge transfer ability between the conventional metal electrode and gas sensing semiconductors. Herein, for the first time, a fully flexible paper-based gas sensor integrated with the Ti₃C₂T_x-MXene nonmetallic electrode and the Ti₃C₂T_x/WS₂ gas sensing film was designed to form Ohmic contact and Schottky heterojunction in a single gas sensing channel. Ti₃C₂T_x/WS₂ has outstanding physical and chemical properties for both Ti₃C₂T_x and WS₂ nanoflakes, showing high conductivity, effective charge transfer, and abundant active sites for gas sensing. The response of the gas sensor to NO₂ (1 ppm) at room temperature is 15.2%, which is about 3.2 and 76.0 times as high as that of the Au interdigital electrode integrated with the Ti₃C₂T_x/WS₂ sensor (4.8%) and the MXene electrode integrated with the Ti₃C₂T_x sensor (0.2%), respectively. Besides, this design performed at a limit of detection with 11.0 ppb NO₂ gas and displayed excellent stability under high humidities. Based on first-principles density functional theory calculation results, the improvement of the gas sensing performance can be mainly attributed to the heterojunction regulation effect, work function matching, and suppressing metal-induced gap states. This work provides a new approach for the design of flexible gas sensors on paper with MXene-based conductive electrodes and gas sensing materials.

A Flexible Tribotronic Artificial Synapse with Bioinspired Neurosensory Behavior

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

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

HIGHLIGHTS

Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human-machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Human-machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human-machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human-machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.

O, Zettl A, Lin L. Zinc stannate microcubes with integrated microheater for low-temperature NO2 detection

This paper reports a facile technique to construct an oxide nanostructured film on a low-power microheater sensor platform to detect the NO2 gas with high sensitivity and selectivity at a...