A bioinspired optoelectronically engineered artificial neurorobotics device with sensorimotor functionalities

Low-voltage flexible organic transistors utilizing passivated polyelectrolyte dielectrics for tactile sensing and braille recognition

Organic field-effect transistors (OFETs) utilizing polyelectrolyte dielectrics offer low-power operation and good signal amplification, making them well suited to pressure sensor applications. However, pristine polyelectrolyte dielectrics suffer from high leakage currents and instability under device operation. Here, we develop novel passivated polyelectrolyte dielectrics to enhance electrical performance of low-voltage flexible OFETs, achieving the reduced hysteresis and the significantly improved mobility from 0.04 to 1.17 cm2 V-1 s-1. The optimized OFETs with passivated polyelectrolyte dielectrics are integrated as flexible pressure sensors, affording high sensitivity (297.2 kPa-1) at -2 V, wide response range (0-70.1 kPa), and fast response in milliseconds. Inspired by human skin, the OFET pressure sensor successfully simulates biological synaptic behaviors, including excitatory postsynaptic currents, paired-pulse facilitation, and the transition from short-term memory to long-term memory, which are crucial for processing complex signals. Furthermore, intelligent touch recognition of braille characters with an accuracy of ∼98% is achieved by designing the low-voltage OFET pressure sensor array and combining it with algorithms. These results provide new insights into advanced dielectrics and low-power OFET sensors for potential applications in skin-like artificial synapses, and human-machine interaction systems.

Precise weight tuning in quantum dot-based resistive-switching memory for neuromorphic systems

In this study, nonvolatile bipolar resistive switching and synaptic emulation behaviors are performed in an InGaP quantum dots (QDs)/HfO2-based memristor device. First, the physical and chemical properties of InGaP QDs are investigated by high-resolution transmission electron microscopy and spectrophotometric analysis. Through comparative experiments, it is proven that the HfO2 layer improves the variations in resistive switching characteristics. Additionally, the Al/QDs/HfO2/ITO device exhibits reversible switching performances with excellent data retention. Fast switching speeds in the order of nanoseconds were confirmed, which could be explained by trapping/detrapping and quantum tunneling effects by the trap provided by nanoscale InGaP QDs. In addition, the operating voltage is decreased when the device is exposed to ultraviolet light for low-power switching. Biological synapse features such as spike-timing-dependent plasticity are emulated for neuromorphic systems. Finally, the incremental step pulse using proven algorithm method enabled the implementation of four-bit states (16 states), markedly enhancing the inference precision of neuromorphic systems.

All-Optically Controlled Memristive Device Based on Cu2O/TiO2 Heterostructure Toward Neuromorphic Visual System

The optoelectronic memristor integrates the multifunctionalities of image sensing, storage, and processing, which has been considered as the leading candidate to construct novel neuromorphic visual system. In particular, memristive materials with all-optical modulation and complementary metal oxide semiconductor (CMOS) compatibility are highly desired for energy-efficient image perception. As a p-type oxide material, Cu2O exhibits outstanding theoretical photoelectric conversion efficiency and broadband photoresponse. In this work, an all-optically controlled memristor based on the Cu2O/TiO2/sodium alginate nanocomposite film is developed. Optical potentiation and depression behaviors have been implemented by utilizing visible (680 nm) and ultraviolet (350 nm) light. Furthermore, a 7 × 9 optoelectronic memristive array with satisfactory device variation and environment stability is constructed to emulate the image preprocessing function in biological retina. The random noise can be reduced effectively by utilizing bidirectional optical input. Beneficial from the image preprocessing function, the accuracy of handwritten digit classification increases more than 60%. Our work presents a pathway toward high-efficient neuromorphic visual system and promotes the development of artificial intelligence technology.

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.

Tunable synaptic behaviors of solution-processed InGaO films for artificial visual systems

Due to their persistent photoconductivity, amorphous metal oxide thin films are promising for construction of artificial visual systems. In this work, large-scale, uniformly distributed amorphous InGaO thin films with an adjustable In/Ga ratio and thickness are prepared successfully by a low-cost environmentally friendly and easy-to-handle solution process for constructing artificial visual systems. With the increase of the In/Ga ratio and film thickness, the number of oxygen vacancies increases, along with the increase of post-synaptic current triggered by illumination, benefiting the transition of short-term plasticity to long-term plasticity. With an optimal In/Ga ratio and film thickness, the conductance response difference at a decay of 0 s between the 1st and the 10th views of a 5 × 5 array InGaO thin film transistor is up to 2.88 μA, along with an increase in the Idecay 30s/Idecay 0s ratio from 45.24% to 53.24%, resulting in a high image clarity and non-volatile artificial visual memory. Furthermore, a three-layer artificial vision network is constructed to evaluate the image recognition capability, exhibiting an accuracy of up to 91.32%. All results promise low-cost and easy-to-handle amorphous InGaO thin films for future visual information processing and image recognition.

Versatile Papertronics: Photo-Induced Synapse and Security Applications on Papers

Paper is a readily available material in nature. Its recyclability, eco-friendliness, portability, flexibility, and affordability make it a favored substrate for researchers seeking cost-effective solutions. Electronic devices based on solution process are fabricated on paper and banknotes using PVK and SnO2 nanoparticles. The devices manufactured on paper substrates exhibit photosynaptic behavior under ultraviolet pulse illumination, stemming from numerous interactions on the surface of the SnO2 nanoparticles. A light-modulated artificial synapse device is realized on a paper at a low voltage bias of -0.01 V, with an average recognition rate of 91.7% based on the Yale Face Database. As a security device on a banknote, 400 devices in a 20 × 20 array configuration exhibited random electrical characteristics owing to the local morphology of the SnO2 nanoparticles and differences in the depletion layer width at the SnO2/PVK interface. The security Physically Unclonable Functions (PUF) key based on the current distribution extracted at -1 V show unpredictable reproducibility with 50% uniformity, 48.7% inter-Hamming distance, and 50.1% bit-aliasing rates. Moreover, the device maintained its properties for more than 210 days under a curvature radius of 8.75 mm and bias and UV irradiation stress conditions.

A Plasmonic Optoelectronic Resistive Random-Access Memory for In-Sensor Color Image Cryptography

The optoelectronic resistive random-access memory (RRAM) with the integrated function of perception, storage and intrinsic randomness displays promising applications in the hardware level in-sensor image cryptography. In this work, 2D hexagonal boron nitride based optoelectronic RRAM is fabricated with semitransparent noble metal (Ag or Au) as top electrodes, which can simultaneous capture color image and generate physically unclonable function (PUF) key for in-sensor color image cryptography. Surface plasmons of noble metals enable the strong light absorption to realize an efficient modulation of filament growth at nanoscale. Resistive switching curves show that the optical stimuli can impede the filament aggregation and promote the filament annihilation, which originates from photothermal effects and photogenerated hot electrons in localized surface plasmon resonance of noble metals. By selecting noble metals, the optoelectronic RRAM array can respond to distinct wavelengths and mimic the biological dichromatic cone cells to perform the color perception. Due to the intrinsic and high-quality randomness, the optoelectronic RRAM can produce a PUF key in every exposure cycle, which can be applied in the reconfigurable cryptography. The findings demonstrate an effective strategy to build optoelectronic RRAM for in-sensor color image cryptography applications.

Volume-Metallization 3D-Printed Polymer Composites

3D printing polymer or metal can achieve complicated structures while lacking multifunctional performance. Combined printing of polymer and metal is desirable and challenging due to their insurmountable mismatch in melting-point temperatures. Here, a novel volume-metallization 3D-printed polymer composite (VMPC) with bicontinuous phases for enabling coupled structure and function, which are prepared by infilling low-melting-point metal (LM) to controllable porous configuration is reported. Based on vacuum-assisted low-pressure conditions, LM is guided by atmospheric pressure action and overcomes surface tension to spread along the printed polymer pore channel, enabling the complete filling saturation of porous structures for enhanced tensile strength (up to 35.41 MPa), thermal (up to 25.29 Wm-1K-1) and electrical (>106 S m-1) conductivities. The designed 3D-printed microstructure-oriented can achieve synergistic anisotropy in mechanics (1.67), thermal (27.2), and electrical (>1012) conductivities. VMPC multifunction is demonstrated, including customized 3D electronics with elevated strength, electromagnetic wave-guided transport and signal amplification, heat dissipation device for chip temperature control, and storage components for thermoelectric generator energy conversion with light-heat-electricity.

Engineered CpG-Loaded Nanorobots Drive Autophagy-Mediated Immunity for TLR9-Positive Cancer Therapy

Smart nanorobots have emerged as novel drug delivery platforms in nanomedicine, potentially improving anti-cancer efficacy and reducing side effects. In this study, an intelligent tumor microenvironment-responsive nanorobot is developed that effectively delivers CpG payloads to Toll-like receptor 9 (TLR9)-positive tumors to induce autophagy-mediated cell death for immunotherapy. The nanorobots are fabricated by co-self-assembly of two amphiphilic triblock polymer peptides: one containing the matrix metallopeptidase 2 (MMP2)-cleaved GPLGVRGS motif to control the mechanical opening of the nanorobots and provide targeting capability for TLR-9-positive tumors and the other consisting of an arginine-rich GRRRDRGRS sequence that can condense nuclear acid payloads through electrostatic interactions. Using multiple tumor-bearing mouse models, it is investigated whether the intravenous injection of CpG-loaded nanorobots could effectively deliver CpG payloads to TLR-9-positive tumors and elicit anti-tumor immunity through TLR9 signaling and autophagy. Therefore, besides being a commonly used adjuvant for tumor vaccination, CpG-loaded nanorobots can effectively reprogram the tumor immunosuppressive microenvironment and suppress tumor growth and recurrence. This nanorobot-based CpG immunotherapy can be considered a feasible approach to induce anti-tumor immunity, showing great therapeutic potential for the future treatment of TLR9-positive cancers.

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.

Recent progress in multifunctional, reconfigurable, integrated liquid metal-based stretchable sensors and standalone systems

Possessing a unique combination of properties that are traditionally contradictory in other natural or synthetical materials, Ga-based liquid metals (LMs) exhibit low mechanical stiffness and flowability like a liquid, with good electrical and thermal conductivity like metal, as well as good biocompatibility and room-temperature phase transformation. These remarkable properties have paved the way for the development of novel reconfigurable or stretchable electronics and devices. Despite these outstanding properties, the easy oxidation, high surface tension, and low rheological viscosity of LMs have presented formidable challenges in high-resolution patterning. To address this challenge, various surface modifications or additives have been employed to tailor the oxidation state, viscosity, and patterning capability of LMs. One effective approach for LM patterning is breaking down LMs into microparticles known as liquid metal particles (LMPs). This facilitates LM patterning using conventional techniques such as stencil, screening, or inkjet printing. Judiciously formulated photo-curable LMP inks or the introduction of an adhesive seed layer combined with a modified lift-off process further provide the micrometer-level LM patterns. Incorporating porous and adhesive substrates in LM-based electronics allows direct interfacing with the skin for robust and long-term monitoring of physiological signals. Combined with self-healing polymers in the form of substrates or composites, LM-based electronics can provide mechanical-robust devices to heal after damage for working in harsh environments. This review provides the latest advances in LM-based composites, fabrication methods, and their novel and unique applications in stretchable or reconfigurable sensors and resulting integrated systems. It is believed that the advancements in LM-based material preparation and high-resolution techniques have opened up opportunities for customized designs of LM-based stretchable sensors, as well as multifunctional, reconfigurable, highly integrated, and even standalone systems.

Liquid Metal-Polymer Conductor-Based Conformal Cyborg Devices

Gallium-based liquid metal (LM) exhibits exceptional properties such as high conductivity and biocompatibility, rendering it highly valuable for the development of conformal bioelectronics. When combined with polymers, liquid metal-polymer conductors (MPC) offer a versatile platform for fabricating conformal cyborg devices, enabling functions such as sensing, restoration, and augmentation within the human body. This review focuses on the synthesis, fabrication, and application of MPC-based cyborg devices. The synthesis of functional materials based on LM and the fabrication techniques for MPC-based devices are elucidated. The review provides a comprehensive overview of MPC-based cyborg devices, encompassing their applications in sensing diverse signals, therapeutic interventions, and augmentation. The objective of this review is to serve as a valuable resource that bridges the gap between the fabrication of MPC-based conformal devices and their potential biomedical applications.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

Solid-Liquid Interfacial Engineered Large-Area Two-Dimensional Covalent Organic Framework Films

Constructing uniform covalent organic framework (COF) film on substrates for electronic devices is highly desirable. Here, a simple and mild strategy is developed to prepare them by polymerization on a solid-liquid interface. The universality of the method is confirmed by the successful preparation of five COF films with different microstructures. These films have large lateral size, controllable thickness, and high crystalline quality. And COF patterns can also be directly achieved on substrates via hydrophilic and hydrophobic interface engineering, which is in favor of preparing device array. For application studies, the PyTTA-TPA (PyTTA: 4,4',4'',4'''-(1,3,6,8-Tetrakis(4-aminophenyl)pyrene and TPA: terephthalaldehyde) COF film has a high photoresponsivity of 59.79 μA W-1 at 420 nm for photoelectrochemical (PEC) detection. When employed as an active material for optoelectronic synaptic devices for the first attempt, it shows excellent light-stimulated synaptic plasticity properties such as short-term plasticity (STP), long-term plasticity (LTP), and the conversion of STP to LTP, which can be used to simulate biological synaptic functions.

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.

Mixed-Dimensional Nanoparticle-Nanowire Channels for Flexible Optoelectronic Artificial Synapse with Enhanced Photoelectric Response and Asymmetric Bidirectional Plasticity

A mixed-dimensional dual-channel synaptic transistor composed of inorganic nanoparticles and organic nanowires was fabricated to expand the photoelectric gain range. The device can actualize the sensitization features of the nociceptor and shows improved responsiveness to visible light. Under electrical pulses with different polarities, the apparatus exhibits reconfigurable asymmetric bidirectional plasticity. Moreover, the devices demonstrate good operational tolerance and mechanical stability, retaining more than 60% of their maximum responsiveness after 100 consecutive/bidirectional and 1000 flex/flat operations. The improved photoelectric response of the device endows a high image recognition accuracy of greater than 80%. Asymmetric bidirectional plasticity is used as punishment/reward in a psychological experiment to emulate the improvement of learning motivation and enables real-time forward and backward deflection (+7 and -25°) of artificial muscle. The mixed-dimensional optoelectronic artificial synapses with switchable behavior and electron/hole transport type have important prospects for neuromorphic processing and artificial somatosensory nerves.

Sonochemistry of Liquid-Metal Galinstan toward the Synthesis of Two-Dimensional and Multilayered Gallium-Based Metal-Oxide Photonic Semiconductors

The scientific field of two-dimensional (2D) nanostructures has witnessed tremendous development during the last decade. To date, different synthesis approaches have been developed; therefore, various exceptional properties of this family of advanced materials have been discovered. It has recently been found that the natural surface oxide films of room-temperature liquid metals is an emerging platform for the synthesis of novel types of 2D nanostructures with numerous functional applications. However, most of the developed synthesis techniques for these materials are based on the direct mechanical exfoliation of 2D materials as research targets. This paper reports a facile and functional sonochemical-assisted approach for the synthesis of 2D hybrid and complex multilayered nanostructures with tunable characteristics. In this method, the intense interaction of acoustic waves with microfluidic gallium-based room-temperature liquid galinstan alloy provides the activation energy for synthesis of hybrid 2D nanostructures. The microstructural characterizations reveal the impact of sonochemical synthesis parameters, including the processing time and composition of the ionic synthesis environment, on the growth of Ga_xO_y/Se 2D hybrid structures and InGa_xO_y/Se multilayered crystalline structures with tunable photonic characteristics. This technique shows promising potential for synthesis of various types of 2D and layered semiconductor nanostructures with tunable photonic characteristics.

Optoelectronic Synaptic Memtransistor Based on 2D SnSe/MoS2 van der Waals Heterostructure under UV-Ozone Treatment

Memristive switching devices with electrically and optically invoked synaptic behaviors show great promise in constructing an artificial biological visual system. Through rational design and integration, 2D materials and their van der Waals (vdW) heterostructures can be applied to realize multifunctional optoelectronic devices. Here, a multifunctional optoelectronic synaptic memtransistor based on a SnSe/MoS₂ vdW p-n heterojunction to simulate the human biological visual system is reported. By employing simple mild UV-ozone treatment, the device exhibits reversible resistive switching (RS) behavior with switching ratio up to 10³ . The retina-like selective response to different input light wavelengths is activated, as well as programmable multilevel resistance states and long-term synaptic plasticity. Moreover, memory and logic functions analogous to those found in the visual cortex of the brain are performed by controlling the optical and electrical input signals. This work proposes a feasible strategy to modulate RS in vdW heterostructures for memristive devices, which show significant potential for neuromorphic processing.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

Plasmonic Nanodomains Decorated on Two-Dimensional Oxide Semiconductors for Photonic-Assisted CO2 Conversion

Plasmonic nanostructures ensure the reception and harvesting of visible lights for novel photonic applications. In this area, plasmonic crystalline nanodomains decorated on the surface of two-dimensional (2D) semiconductor materials represent a new class of hybrid nanostructures. These plasmonic nanodomains activate supplementary mechanisms at material heterointerfaces, enabling the transfer of photogenerated charge carriers from plasmonic antennae into adjacent 2D semiconductors and therefore activate a wide range of visible-light assisted applications. Here, the controlled growth of crystalline plasmonic nanodomains on 2D Ga₂O₃ nanosheets was achieved by sonochemical-assisted synthesis. In this technique, Ag and Se nanodomains grew on 2D surface oxide films of gallium-based alloy. The multiple contribution of plasmonic nanodomains enabled the visible-light-assisted hot-electron generation at 2D plasmonic hybrid interfaces, and therefore considerably altered the photonic properties of the 2D Ga₂O₃ nanosheets. Specifically, the multiple contribution of semiconductor-plasmonic hybrid 2D heterointerfaces enabled efficient CO₂ conversion through combined photocatalysis and triboelectric-activated catalysis. The solar-powered acoustic-activated conversion approach of the present study enabled us to achieve the CO₂ conversion efficiency of more than 94% in the reaction chambers containing 2D Ga₂O₃-Ag nanosheets.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Development of a palm-sized bioelectronic sensing device for protein detection in milk samples

The present study relates a portable optical sensing device supported by a small single-board (SBC) computer. The electronic architectural avenue connects the SBC with a camera, LED lights and a monitor. A 'sensor integration unit' has been linked with the device where the biological reactions were performed and assessed based on the concentration-dependent optical signal outputs. This setup can detect the generation of colors and distinguish their changes in the RGB intensity scale with an accuracy of a single pixel unit. A predefined range of values was obtained and fed to the device that can quantitatively sense the molecule of interest on the sensing matrix. The device has a touchscreen interactive panel that allows users to manually set experimental conditions and connect the entire measurement process to the cloud storage for backup information. We have considered detecting Alkaline Phosphatase (ALP) quantitatively from standard solutions as well as in milk samples as a proof-of-concept protein molecule. The device has shown exceptional analytical performance for lower and higher concentration ranges (0-100 U/mL and 100-1000 U/mL) with correlation coefficient values of 0.99. The detection limit of ALP was determined to be 0.1 U/mL, and the average time of a sample assessment was recorded to be 15 s. The device has also been tested against ALP-spiked milk samples to check its effectiveness and commercial viability. The outcome of the real-time assessment was sensitive and efficient, indicating its direct commercial and clinical importance towards colorimetric detection for diverse macromolecules.

Low-Dimensional-Materials-Based Flexible Artificial Synapse: Materials, Devices, and Systems

With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human-machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.

GaOOH Crystallite Growth on Liquid Metal Microdroplets in Water: Influence of the Local Environment

Gallium-based liquid metals form alloys with a melting point close to or below room temperature. On the surface of these liquid metals, a thin oxide skin is formed once in contact with oxygen, and this oxide skin can be leveraged to stabilize liquid metal micro- and nanodroplets in a liquid. During sonication and storage of these droplets in aqueous solution, gallium oxide hydroxide (GaOOH) forms on these droplets, and given enough time or treatment with heat, a full shape transition and dealloying are observed. In this article, we show that GaOOH can be grown at room temperature and that the growth is dependent on both the local environment and temperature. GaOOH growth on liquid metal microdroplets located at the air/water interface is considerably faster than in the bulk phase. Interestingly, hydrolysis to GaOOH is hampered and stops at 15 °C in bulk water after 6 h. In contrast, hydrolysis commences even at 15 °C for liquid metal microdroplets located at the air/water interface, and full surface coverage is obtained after around 24 h (compared to 12 h at 25 °C at the air/water interface). The X-ray photoelectron spectroscopy (XPS) measurement suggests that gallium oxide is dissolved and Ga(OH)₃ is formed as a precursor that reacts in a downstream reaction toward GaOOH. This improved understanding of the GaOOH formation can be leveraged to control the liquid metal micro- and nanodroplet shape and composition (i.e., for biomedical applications).

Recent Synergies of Machine Learning and Neurorobotics: A Bibliometric and Visualized Analysis

Over the past decade, neurorobotics-integrated machine learning has emerged as a new methodology to investigate and address related problems. The combined use of machine learning and neurorobotics allows us to solve problems and find explanatory models that would not be possible with traditional techniques, which are basic within the principles of symmetry. Hence, neuro-robotics has become a new research field. Accordingly, this study aimed to classify existing publications on neurorobotics via content analysis and knowledge mapping. The study also aimed to effectively understand the development trend of neurorobotics-integrated machine learning. Based on data collected from the Web of Science, 46 references were obtained, and bibliometric data from 2013 to 2021 were analyzed to identify the most productive countries, universities, authors, journals, and prolific publications in neurorobotics. CiteSpace was used to visualize the analysis based on co-citations, bibliographic coupling, and co-occurrence. The study also used keyword network analysis to discuss the current status of research in this field and determine the primary core topic network based on cluster analysis. Through the compilation and content analysis of specific bibliometric analyses, this study provides a specific explanation for the knowledge structure of the relevant subject area. Finally, the implications and future research context are discussed as references for future research.

Neurorobotic approaches to emulate human motor control with the integration of artificial synapse

The advancement of electronic devices has enabled researchers to successfully emulate human synapses, thereby promoting the development of the research field of artificial synapse integrated soft robots. This paper proposes an artificial reciprocal inhibition system that can successfully emulate the human motor control mechanism through the integration of artificial synapses. The proposed system is composed of artificial synapses, load transistors, voltage/current amplifiers, and a soft actuator to demonstrate the muscle movement. The speed, range, and direction of the soft actuator movement can be precisely controlled via the preset input voltages with different amplitudes, numbers, and signs (positive or negative). The artificial reciprocal inhibition system can impart lifelike motion to soft robots and is a promising tool to enable the successful integration of soft robots or prostheses in a living body.

Parallel transmission in a synthetic nerve

Bioelectronic devices that are tetherless and soft are promising developments in medicine, robotics and chemical computing. Here, we describe bioinspired synthetic neurons, composed entirely of soft, flexible biomaterials, capable of rapid electrochemical signal transmission over centimetre distances. Like natural cells, our synthetic neurons release neurotransmitters from their terminals, which initiate downstream reactions. The components of the neurons are nanolitre aqueous droplets and hydrogel fibres, connected through lipid bilayers. Transmission is powered at these interfaces by light-driven proton pumps and mediated by ion-conducting protein pores. By bundling multiple neurons into a synthetic nerve, we have shown that distinct signals can propagate simultaneously along parallel axons, thereby transmitting spatiotemporal information. Synthetic nerves might play roles in next-generation implants, soft machines and computing devices.

Intrinsic Properties of Multi-Layer TiO2/V2O5/TiO2 Coatings Prepared via E-Beam Evaporation

Nanocomposite multi-layer TiO2/V2O5/TiO2 thin films were prepared via electron-beam evaporation using high-purity targets (TiO2 and V2O5 purity > 99.9%) at substrate temperatures of 270 °C (TiO2) and 25 °C (V2O5) under a partial pressure of oxygen of 2 × 10−4 mbar to maintain the stoichiometry. Rutherford backscattering spectrometry was used to confirm the layer structure and the optimal stoichiometry of the thin films, with a particle size of 20 to 40 nm. The thin films showed an optical transmittance of ~78% in the visible region and a reflectance of ~90% in the infrared. A decrease in transmittance was observed due to the greater cumulative thickness of the three layers and multiple reflections at the interface of the layers. The optical bandgap of the TiO2 mono-layer was ~3.49 eV, whereas that of the multi-layer TiO2/V2O5/TiO2 reached ~3.51 eV. The increase in the optical bandgap was due to the inter-diffusion of the layers at an elevated substrate temperature during the deposition. The intrinsic, structural, and morphological features of the TiO2/V2O5/TiO2 thin films suggest their efficient use as a solar water heater system.

A Photoelectric Spiking Neuron for Visual Depth Perception

The biological visual system encodes optical information into spikes and processes them by the neural network, which enables the perception with high throughput of visual processing with ultralow energy budget. This has inspired a wide spectrum of devices to imitate such neural process, while precise mimicking such procedure is still highly required. Here, a highly bio-realistic photoelectric spiking neuron for visual depth perception is presented. The firing spikes generated by the TaO_X memristive spiking encoders have a biologically similar frequency range of 1-200 Hz and sub-micro watts power. Such spiking encoder is integrated with a photodetector and a network of neuromorphic transistors, for information collection and recognition tasks, respectively. The distance-dependent response and eye fatigue of biological visual systems have been mimicked based on such photoelectric spiking neuron. The simulated depth perception shows a recognition improvement by adapting to sights at different distances. The results can advance the technologies in bioinspired or robotic systems that may be endowed with depth perception and power efficiency at the same time.

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

2D Material-Based Optical Biosensor: Status and Prospect

The combination of 2D materials and optical biosensors has become a hot research topic in recent years. Graphene, transition metal dichalcogenides, black phosphorus, MXenes, and other 2D materials (metal oxides and degenerate semiconductors) have unique optical properties and play a unique role in the detection of different biomolecules. Through the modification of 2D materials, optical biosensor has the advantages that traditional sensors (such as electrical sensing) do not have, and the sensitivity and detection limit are greatly improved. Here, optical biosensors based on different 2D materials are reviewed. First, various detection methods of biomolecules, including surface plasmon resonance (SPR), fluorescence resonance energy transfer (FRET), and evanescent wave and properties, preparation and integration strategies of 2D material, are introduced in detail. Second, various biosensors based on 2D materials are summarized. Furthermore, the applications of these optical biosensors in biological imaging, food safety, pollution prevention/control, and biological medicine are discussed. Finally, the future development of optical biosensors is prospected. It is believed that with their in-depth research in the laboratory, optical biosensors will gradually become commercialized and improve people's quality of life in many aspects.

Dynamic Self-Rectifying Liquid Metal-Semiconductor Heterointerfaces: A Platform for Development of Bioinspired Afferent Systems

The assembly of geometrically complex and dynamically active liquid metal/semiconductor heterointerfaces has drawn extensive attention in multidimensional electronic systems. In this study the chemovoltaic driven reactions have enabled the microfluidity of hydrophobic galinstan into a three-dimensional (3D) semiconductor matrix. A dynamic heterointerface is developed between the atomically thin surface oxide of galinstan and the TiO₂-Ni interface. Upon the growth of Ga₂O₃ film at the Ga₂O₃-TiO₂ heterointerface, the partial reduction of the TiO₂ film was confirmed by material characterization techniques. The conductance imaging spectroscopy and electrical measurements are used to investigate the charge transfer at heterointerfaces. Concurrently, the dynamic conductance in artificial synaptic junctions is modulated to mimic the biofunctional communication characteristics of multipolar neurons, including slow and fast inhibitory and excitatory postsynaptic responses. The self-rectifying characteristics, femtojoule energy processing, tunable synaptic events, and notably the coordinated signal recognition are the main characteristics of this multisynaptic device. This novel 3D design of liquid metal-semiconductor structure opens up new opportunities for the development of bioinspired afferent systems. It further facilitates the realization of physical phenomena at liquid metal-semiconductor heterointerfaces.

Nanotip Formation from Liquid Metals for Soft Electronic Junctions

Liquid metals and alloys with high-aspect-ratio nanodimensional features are highly sought-after for emerging electronic applications. However, high surface tension, water-like fluidity, and the existence of self-limiting oxides confer specific peculiarities to their characteristics. Here, we introduce a high accuracy nanometric three-dimensional pulling and stretching method to fabricate liquid-metal-based nanotips from room- or near-room-temperature gallium-based alloys. The pulling rate and step size were controlled with a resolution of up to 10 nm and yielded different nanotip morphologies and lengths as a function of the base liquid metal alloy composition and the pulling parameters. The obtained nanotips presented high aspect ratios over lengths of a few microns and apexes between 10 and 100 nm. The liquid metal alloys were found confined within nanotips with about 10 nm apexes when vertically pulled at 100 nm/s. An amorphous gallium oxide skin was shown to cover the surface of the nanotips, while the liquid core was composed of the initial liquid metal alloys. The electrical contact established at the nanotips was characterized under dynamic conditions. The liquid metal nanotips showed an Ohmic resistance when a continuous liquid metal channel was formed, and a controllable semiconductor state corresponding to a heterojunction formed at the junction between the liquid metal phase and the gallium oxide semiconductor skin. The variable threshold voltages of the heterojunction were controlled via stretching of the nanotips with a 10 nm step resolution. The liquid metal nanotips were also used for establishing soft electronic junctions. This novel method of liquid metal nanotip fabrication with Ohmic and semiconducting behaviors will lead to exciting avenues for developing electronic and sensing devices.

Evaluation of peripheral nodal recurrence in patients with endometrial cancer

Objective:

To evaluate the clinico-pathological patient features, prognostic factors, treatment options and outcomes of peripheral nodal recurrence (PNR) of endometrial cancer (EC).

Material and Methods:

The data of nine patients with PNR of EC from two institutions were reviewed. The electronic literature was reviewed from 1972 to May 2018 to identify articles about PNR in EC. Finally, 42 cases were evaluated.

Results:

Nineteen (45.2%) patients were initially diagnosed with either stage I or II disease, whereas 20 (47.7%) patients had stage III or IV disease while the stages were not reported in three (7.1%). PNR developed as the first recurrence in 40 (95.2%) patients and as the second recurrence in 2 (4.8%) patients. Isolated PNR appeared in 35 (83.3%). Seven (16.7%) had PNR coexisting with multiple other sites of tumoral involvement. In the entire cohort, the 5-year and 10-year post-recurrence survival (PRS) were both 78%. Only the presence of distant hematogenous metastasis concurrent with PNR was significantly related to poor PRS (p=0.005). Among patients with isolated PNR, those who had surgery had 30% greater 5-year PRS than those treated without surgery, but this difference was not significant (80% vs 50%; p>0.05).

Conclusion:

A concurrent distant hematogenous metastasis was the only factor related to poor survival. A wide range of therapies exists for PNR but none of the therapies appear to be more advantageous than another. However, surgery as a component of treatment can render a survival advantage for patients who have isolated PNR.

2D Semiconductor Nanomaterials and Heterostructures: Controlled Synthesis and Functional Applications

Two-dimensional (2D) semiconductors beyond graphene represent the thinnest stable known nanomaterials. Rapid growth of their family and applications during the last decade of the twenty-first century have brought unprecedented opportunities to the advanced nano- and opto-electronic technologies. In this article, we review the latest progress in findings on the developed 2D nanomaterials. Advanced synthesis techniques of these 2D nanomaterials and heterostructures were summarized and their novel applications were discussed. The fabrication techniques include the state-of-the-art developments of the vapor-phase-based deposition methods and novel van der Waals (vdW) exfoliation approaches for fabrication both amorphous and crystalline 2D nanomaterials with a particular focus on the chemical vapor deposition (CVD), atomic layer deposition (ALD) of 2D semiconductors and their heterostructures as well as on vdW exfoliation of 2D surface oxide films of liquid metals.

Flexible Artificial Sensory Systems Based on Neuromorphic Devices

Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.

Retina-Inspired Organic Heterojunction-Based Optoelectronic Synapses for Artificial Visual Systems

For the realization of retina-inspired neuromorphic visual systems which simulate basic functions of human visual systems, optoelectronic synapses capable of combining perceiving, processing, and memorizing in a single device have attracted immense interests. Here, optoelectronic synaptic transistors based on tris(2-phenylpyridine) iridium (Ir(ppy)₃) and poly(3,3-didodecylquarterthiophene) (PQT-12) heterojunction structure are presented. The organic heterojunction serves as a basis for distinctive synaptic characteristics under different wavelengths of light. Furthermore, synaptic transistor arrays are fabricated to demonstrate their optical perception efficiency and color recognition capability under multiple illuminating conditions. The wavelength-tunability of synaptic behaviors further enables the mimicry of mood-modulated visual learning and memorizing processes of humans. More significantly, the computational dynamics of neurons of synaptic outputs including associated learning and optical logic functions can be successfully demonstrated on the presented devices. This work may locate the stage for future studies on optoelectronic synaptic devices toward the implementation of artificial visual systems.

Mimicking efferent nerves using a graphdiyne-based artificial synapse with multiple ion diffusion dynamics

A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has been proposed to mimic biological signal transmission behavior. The impulse response of the GAS has been reduced to several millivolts with competitive femtowatt-level consumption, exceeding the biological level by orders of magnitude. Most importantly, the GAS is capable of parallelly processing signals transmitted from multiple pre-neurons and therefore realizing dynamic logic and spatiotemporal rules. It is also found that the GAS is thermally stable (at 353 K) and environmentally stable (in a relative humidity up to 35%). Our artificial efferent nerve, connecting the GAS with artificial muscles, has been demonstrated to complete the information integration of pre-neurons and the information output of motor neurons, which is advantageous for coalescing multiple sensory feedbacks and reacting to events. Our synaptic element has potential applications in bioinspired peripheral nervous systems of soft electronics, neurorobotics, and biohybrid systems of brain-computer interfaces.

Solution-processed electronics for artificial synapses

Artificial synaptic devices and systems have become hot topics due to parallel computing, high plasticity, integration of storage, and processing to meet the challenges of the traditional Von Neumann computers. Currently, two-terminal memristors and three-terminal transistors have been mainly developed for high-density storage with high switching speed and high reliability because of the adjustable resistivity, controllable ion migration, and abundant choices of functional materials and fabrication processes. To achieve the low-cost, large-scale, and easy-process fabrication, solution-processed techniques have been extensively employed to develop synaptic electronics towards flexible and highly integrated three-dimensional (3D) neural networks. Herein, we have summarized and discussed solution-processed techniques in the fabrication of two-terminal memristors and three-terminal transistors for the application of artificial synaptic electronics mainly reported in the recent five years from the view of fabrication processes, functional materials, electronic operating mechanisms, and system applications. Furthermore, the challenges and prospects were discussed in depth to promote solution-processed techniques in the future development of artificial synapse with high performance and high integration.

Recent advances in bioelectronics chemistry

Research in bioelectronics is highly interdisciplinary, with many new developments being based on techniques from across the physical and life sciences. Advances in our understanding of the fundamental chemistry underlying the materials used in bioelectronic applications have been a crucial component of many recent discoveries. In this review, we highlight ways in which a chemistry-oriented perspective may facilitate novel and deep insights into both the fundamental scientific understanding and the design of materials, which can in turn tune the functionality and biocompatibility of bioelectronic devices. We provide an in-depth examination of several developments in the field, organized by the chemical properties of the materials. We conclude by surveying how some of the latest major topics of chemical research may be further integrated with bioelectronics.

Recent advances in bioelectronics chemistry

Research in bioelectronics is highly interdisciplinary, with many new developments being based on techniques from across the physical and life sciences. Advances in our understanding of the fundamental chemistry underlying the materials used in bioelectronic applications have been a crucial component of many recent discoveries. In this review, we highlight ways in which a chemistry-oriented perspective may facilitate novel and deep insights into both the fundamental scientific understanding and the design of materials, which can in turn tune the functionality and biocompatibility of bioelectronic devices. We provide an in-depth examination of several developments in the field, organized by the chemical properties of the materials. We conclude by surveying how some of the latest major topics of chemical research may be further integrated with bioelectronics.

Spike Encoding with Optic Sensory Neurons Enable a Pulse Coupled Neural Network for Ultraviolet Image Segmentation

Drawing inspiration from biology, neuromorphic systems are of great interest in direct interaction and efficient processing of analogue signals in the real world and could be promising for the development of smart sensors. Here, we demonstrate an artificial sensory neuron consisting of an InGaZnO₄ (IGZO₄)-based optical sensor and NbO_x-based oscillation neuron in series, which can simultaneously sense the optical information even beyond the visible light region and encode them into electrical impulses. Such artificial vision sensory neurons can convey visual information in a parallel manner analogous to biological vision systems, and the output spikes can be effectively processed by a pulse coupled neural network, demonstrating the capability of image segmentation out of a complex background. This study could facilitate the construction of artificial visual systems and pave the way for the development of light-driven neurorobotics, bioinspired optoelectronics, and neuromorphic computing.

Nano-engineering and functionalization of hybrid Au-MexOy-TiO2 (Me = W, Ga) hetero-interfaces for optoelectronic receptors and nociceptors

Bio-inspired nano-electronic devices are key instruments for the development of advanced artificial intelligence systems, which will shape the future of humanoid nano-robotics. An emerging demand is realized for an accurate reception of environmental stimuli via visual perception, processing and realization of optical signals. The present study demonstrates the capability of functionalized all-oxide heterostructured two-dimensional (2D) plasmonic devices for the self-adaptive recognition of visual optical pulses. Specifically, the nano-engineering of the metal/semiconductor interface and co-modulation of heterostructured 2D semiconductor hetero-interfaces of Au/WO3 : TiO2 and Au/Ga2O3 : TiO2 facilitated the receptive and nociceptive detection of visible light pulses. A decrease in the dark current of the Au/WO3 : TiO2 unit resulted in the development of sensitive visible light photoreceptors. Furthermore, the modulation of charge transfers at the Au/Ga2O3 : TiO2 hetero-interfaces were the key parameter to determine the optical reception characteristics and nociceptive performance of all-oxide optoelectronic devices. Specifically, the rapid thermal annealing (RTA) of 2D Ga2O3 in N2 atmosphere ensured the modulation of charge transfer at Au/Ga2O3 : TiO2 hetero-interfaces in plasmonic devices. Thus, hetero-interface engineering enabled the effective control of charge transfer at 2D hetero-interfaces for an adaptive perception of visible optical pulses. Consequently, the fabricated sensitive Au/Ga2O3 (N2) : TiO2 bio-inspired unit emulated the optical functionalities of corneal nociceptors.

A Super-Stretchable Liquid Metal Foamed Elastomer for Tunable Control of Electromagnetic Waves and Thermal Transport

It is remarkably desirable and challenging to design a stretchable conductive material with tunable electromagnetic-interference (EMI) shielding and heat transfer for applications in flexible electronics. However, the existing materials sustained a severe attenuation of performances when largely stretched. Here, a super-stretchable (800% strain) liquid metal foamed elastomer composite (LMF-EC) is reported, achieving super-high electrical (≈104 S cm-1) and thermal (17.6 W mK-1) conductivities under a large strain of 400%, which also exhibits unexpected stretching-enhanced EMI shielding effectiveness of 85 dB due to the conductive network elongation and reorientation. By varying the liquid and solid states of LMF, the stretching can enable a multifunctional reversible switch that simultaneously regulates the thermal, electrical, and electromagnetic wave transport. Novel flexible temperature control and a thermoelectric system based on LMF-EC is furthermore developed. This work is a significant step toward the development of smart electromagnetic and thermal regulator for stretchable electronics.

A Super-Stretchable Liquid Metal Foamed Elastomer for Tunable Control of Electromagnetic Waves and Thermal Transport

It is remarkably desirable and challenging to design a stretchable conductive material with tunable electromagnetic-interference (EMI) shielding and heat transfer for applications in flexible electronics. However, the existing materials sustained a severe attenuation of performances when largely stretched. Here, a super-stretchable (800% strain) liquid metal foamed elastomer composite (LMF-EC) is reported, achieving super-high electrical (≈10⁴ S cm^-1) and thermal (17.6 W mK^-1) conductivities under a large strain of 400%, which also exhibits unexpected stretching-enhanced EMI shielding effectiveness of 85 dB due to the conductive network elongation and reorientation. By varying the liquid and solid states of LMF, the stretching can enable a multifunctional reversible switch that simultaneously regulates the thermal, electrical, and electromagnetic wave transport. Novel flexible temperature control and a thermoelectric system based on LMF-EC is furthermore developed. This work is a significant step toward the development of smart electromagnetic and thermal regulator for stretchable electronics.

Liquid Metal-Based Route for Synthesizing and Tuning Gas-Sensing Elements

There is a strong demand for developing tunable and facile routes for synthesizing gas-sensitive semiconducting compounds. The concept of synthesizing micro- and nanoparticles of metallic compounds in a tunable process, which relies on liquid metals, is presented here. This is a liquid-based ultrasonication procedure within which additional metallic elements (In, Sn, and Zn) are incorporated into liquid Ga that is sonicated in a secondary solvent. We investigate liquid metal sonication in dimethyl sulfoxide (DMSO) and water to show their impact on the size, morphology, and crystal structure of the particulated products. The synthesized materials are annealed to investigate their responses to model reducing (H₂) and oxidizing (NO₂) gas species. The preparation process in DMSO gives rise to predominantly monoclinic Ga₂O₃ crystals which are favorable for gas sensing, while the emergence of rhombohedral Ga₂O₃ phases from the water sonication process led to inactive samples. The ease of tunability without hazardous precursors during the synthesis procedure is demonstrated. The route presented here can be uniquely employed for designing and engineering on-demand functional materials for sensing applications.

Nanoscale All-Oxide-Heterostructured Bio-inspired Optoresponsive Nociceptor

Retina nociceptor, as a key sensory receptor, not only enables the transport of warning signals to the human central nervous system upon its exposure to noxious stimuli, but also triggers the motor response that minimizes potential sensitization. In this study, the capability of two-dimensional all-oxide-heterostructured artificial nociceptor as a single device with tunable properties was confirmed. Newly designed nociceptors utilize ultra-thin sub-stoichiometric TiO2-Ga2O3 heterostructures, where the thermally annealed Ga2O3 films play the role of charge transfer controlling component. It is discovered that the phase transformation in Ga2O3 is accompanied by substantial jump in conductivity, induced by thermally assisted internal redox reaction of Ga2O3 nanostructure during annealing. It is also experimentally confirmed that the charge transfer in all-oxide heterostructures can be tuned and controlled by the heterointerfaces manipulation. Results demonstrate that the engineering of heterointerfaces of two-dimensional (2D) films enables the fabrication of either high-sensitive TiO2-Ga2O3 (Ar) or high-threshold TiO2-Ga2O3 (N2) nociceptors. The hypersensitive nociceptor mimics the functionalities of corneal nociceptors of human eye, whereas the delayed reaction of nociceptor is similar to high-threshold nociceptive characteristics of human sensory system. The long-term stability of 2D nociceptors demonstrates the capability of heterointerfaces engineering for effective control of charge transfer at 2D heterostructured devices.

Nanoscale Au-ZnO Heterostructure Developed by Atomic Layer Deposition Towards Amperometric H2O2 Detection

Nanoscale Au-ZnO heterostructures were fabricated on 4-in. SiO2/Si wafers by the atomic layer deposition (ALD) technique. Developed Au-ZnO heterostructures after post-deposition annealing at 250 °C were tested for amperometric hydrogen peroxide (H2O2) detection. The surface morphology and nanostructure of Au-ZnO heterostructures were examined by field emission scanning electron microscopy (FE-SEM), Raman spectroscopy, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), etc. Additionally, the electrochemical behavior of Au-ZnO heterostructures towards H2O2 sensing under various conditions is assessed by chronoamperometry and electrochemical impedance spectroscopy (EIS). The results showed that ALD-fabricated Au-ZnO heterostructures exhibited one of the highest sensitivities of 0.53 μA μM-1 cm-2, the widest linear H2O2 detection range of 1.0 μM-120 mM, a low limit of detection (LOD) of 0.78 μM, excellent selectivity under the normal operation conditions, and great long-term stability. Utilization of the ALD deposition method opens up a unique opportunity for the improvement of the various capabilities of the devices based on Au-ZnO heterostructures for amperometric detection of different chemicals.