Multidimensional Deep Ultraviolet (DUV) Synapses Based on Organic/Perovskite Semiconductor Heterojunction Transistors for Antispoofing Facial Recognition Systems.

Reliably discerning real human faces from fake ones, known as antispoofing, is crucial for facial recognition systems. While neuromorphic systems offer integrated sensing-memory-processing functions, they still struggle with efficient antispoofing techniques. Here we introduce a neuromorphic facial recognition system incorporating multidimensional deep ultraviolet (DUV) optoelectronic synapses to address these challenges. To overcome the complexity and high cost of producing DUV synapses using traditional wide-bandgap semiconductors, we developed a low-temperature (≤70 °C) solution process for fabricating DUV synapses based on PEA2PbBr4/C8-BTBT heterojunction field-effect transistors. This method enables the large-scale (4-in.), uniform, and transparent production of DUV synapses. These devices respond to both DUV and visible light, showing multidimensional features. Leveraging the unique ability of the multidimensional DUV synapse (MDUVS) to discriminate real human skin from artificial materials, we have achieved robust neuromorphic facial recognition with antispoofing capability, successfully identifying genuine human faces with an accuracy exceeding 92%.

Solution-Processed Ultralow Voltage Organic Transistors With Sharp Switching for Adaptive Visual Perception.

Neuromorphic visual systems can emulate biological retinal systems to perceive visual information under different levels of illumination, making them have considerable potential for future intelligent vehicles and vision automation. However, the complex circuits and high operating voltages of conventional artificial vision systems present great challenges for device integration and power consumption. Here, bioinspired synaptic transistors based on organic single crystal phototransistors are reported, which exhibit excitation and inhibition synaptic plasticity with time-varying. By manipulating the charge dynamics of the trapping centers of organic crystal-electret vertical stacks, organic transistors can operate below 1 V with record high on/off ratios close to 108 and sharp switching with a subthreshold swing of 59.8 mV dec-1. Moreover, the approach offers visual adaptation with highly localized modulation and over 98.2% recognition accuracy under different illumination levels. These bioinspired visual adaptation transistors offer great potential for simplifying the circuitry of artificial vision systems and will contribute to the development of machine vision applications.

Toward Intelligent Display with Neuromorphic Technology.

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Coaxial 3D Bioprinting Process Research and Performance Tests on Vascular Scaffolds.

Three-dimensionally printed vascularized tissue, which is suitable for treating human cardiovascular diseases, should possess excellent biocompatibility, mechanical performance, and the structure of complex vascular networks. In this paper, we propose a method for fabricating vascularized tissue based on coaxial 3D bioprinting technology combined with the mold method. Sodium alginate (SA) solution was chosen as the bioink material, while the cross-linking agent was a calcium chloride (CaCl2) solution. To obtain the optimal parameters for the fabrication of vascular scaffolds, we first formulated theoretical models of a coaxial jet and a vascular network. Subsequently, we conducted a simulation analysis to obtain preliminary process parameters. Based on the aforementioned research, experiments of vascular scaffold fabrication based on the coaxial jet model and experiments of vascular network fabrication were carried out. Finally, we optimized various parameters, such as the flow rate of internal and external solutions, bioink concentration, and cross-linking agent concentration. The performance tests showed that the fabricated vascular scaffolds had levels of satisfactory degradability, water absorption, and mechanical properties that meet the requirements for practical applications. Cellular experiments with stained samples demonstrated satisfactory proliferation of human umbilical vein endothelial cells (HUVECs) within the vascular scaffold over a seven-day period, observed under a fluorescent inverted microscope. The cells showed good biocompatibility with the vascular scaffold. The above results indicate that the fabricated vascular structure initially meet the requirements of vascular scaffolds.

Inorganic Halide Perovskite Nanowires/Conjugated Polymer Heterojunction-Based Optoelectronic Synaptic Transistors for Dynamic Machine Vision.

Inspired by the retina, artificial optoelectronic synapses have groundbreaking potential for machine vision. The field-effect transistor is a crucial platform for optoelectronic synapses that is highly sensitive to external stimuli and can modulate conductivity. On the basis of the decent optical absorption, perovskite materials have been widely employed for constructing optoelectronic synaptic transistors. However, the reported optoelectronic synaptic transistors focus on the static processing of independent stimuli at different moments, while the natural visual information consists of temporal signals. Here, we report CsPbBrI2 nanowire-based optoelectronic synaptic transistors to study the dynamic responses of artificial synaptic transistors to time-varying visual information for the first time. Moreover, on the basis of the dynamic synaptic behavior, a hardware system with an accuracy of 85% is built to the trajectory of moving objects. This work offers a new way to develop artificial optoelectronic synapses for the construction of dynamic machine vision systems.

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability.

Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information's optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.

The Chimeric Chaoyang-Zika Vaccine Candidate Is Safe and Protective in Mice.

Zika virus (ZIKV) is an emerging flavivirus that causes congenital syndromes including microcephaly and fetal demise in pregnant women. No commercial vaccines against ZIKV are currently available. We previously generated a chimeric ZIKV (ChinZIKV) based on the Chaoyang virus (CYV) by replacing the prME protein of CYV with that of a contemporary ZIKV strain GZ01. Herein, we evaluated this vaccine candidate in a mouse model and showed that ChinZIKV was totally safe in both adult and suckling immunodeficient mice. No viral RNA was detected in the serum of mice inoculated with ChinZIKV. All of the mice inoculated with ChinZIKV survived, while mice inoculated with ZIKV succumbed to infection in 8 days. A single dose of ChinZIKV partially protected mice against lethal ZIKV challenge. In contrast, all the control PBS-immunized mice succumbed to infection after ZIKV challenge. Our results warrant further development of ChinZIKV as a vaccine candidate in clinical trials.

Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction.

Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.

Research on Cartilage 3D Printing Technology Based on SA-GA-HA.

Cartilage damage is difficult to heal and poses a serious problem to human health as it can lead to osteoarthritis. In this work, we explore the application of biological 3D printing to manufacture new cartilage scaffolds to promote cartilage regeneration. The hydrogel made by mixing sodium alginate (SA) and gelatin (GA) has high biocompatibility, but its mechanical properties are poor. The addition of hydroxyapatite (HA) can enhance its mechanical properties. In this paper, the preparation scheme of the SA-GA-HA composite hydrogel cartilage scaffold was explored, the scaffolds prepared with different concentrations were compared, and better formulations were obtained for printing and testing. Mathematical modeling of the printing process of the bracket, simulation analysis of the printing process based on the mathematical model, and adjustment of actual printing parameters based on the results of the simulation were performed. The cartilage scaffold, which was printed using Bioplotter 3D printer, exhibited useful mechanical properties suitable for practical needs. In addition, ATDC-5 cells were seeded on the cartilage scaffolds and the cell survival rate was found to be higher after one week. The findings demonstrated that the fabricated chondrocyte scaffolds had better mechanical properties and biocompatibility, providing a new scaffold strategy for cartilage tissue regeneration.

Novel Semiconductive Ternary Hybrid Heterostructures for Artificial Optoelectronic Synapses.

Synaptic devices that mimic biological synapses are considered as promising candidates for brain-inspired devices, offering the functionalities in neuromorphic computing. However, modulation of emerging optoelectronic synaptic devices has rarely been reported. Herein, a semiconductive ternary hybrid heterostructure is prepared with a D-D'-A configuration by introducing polyoxometalate (POM) as an additional electroactive donor (D') into a metalloviologen-based D-A framework. The obtained material features an unprecedented porous 8-connected bcu-net that accommodates nanoscale [α-SiW12 O40 ]4- counterions, displaying uncommon optoelectronic responses. Besides, the fabricated synaptic device based on this material can achieve dual-modulation of synaptic plasticity due to the synergetic effect of electron reservoir POM and photoinduced electron transfer. And it can successfully simulate learning and memory processes similar to those in biological systems. The result provides a facile and effective strategy to customize multi-modality artificial synapses in the field of crystal engineering, which opens a new direction for developing high-performance neuromorphic devices.

Rapid Generation of Recombinant Flaviviruses Using Circular Polymerase Extension Reaction.

The genus Flavivirus is a group of arthropod-borne single-stranded RNA viruses, which includes important human and animal pathogens such as Japanese encephalitis virus (JEV), Zika virus (ZIKV), Dengue virus (DENV), yellow fever virus (YFV), West Nile virus (WNV), and Tick-borne encephalitis virus (TBEV). Reverse genetics has been a useful tool for understanding biological properties and the pathogenesis of flaviviruses. However, the conventional construction of full-length infectious clones for flavivirus is time-consuming and difficult due to the toxicity of the flavivirus genome to E. coli. Herein, we applied a simple, rapid, and bacterium-free circular polymerase extension reaction (CPER) method to synthesize recombinant flaviviruses in vertebrate cells as well as insect cells. We started with the de novo synthesis of the JEV vaccine strain SA-14-14-2 in Vero cells using CPER, and then modified the CPER method to recover insect-specific flaviviruses (ISFs) in mosquito C6/36 cells. Chimeric Zika virus (ChinZIKV) based on the Chaoyang virus (CYV) backbone and the Culex flavivirus reporter virus expressing green fluorescent protein (CxFV-GFP) were subsequently rescued in C6/36 cells. CPER is a simple method for the rapid generation of flaviviruses and other potential RNA viruses. A CPER-based recovery system for flaviviruses of different host ranges was established, which would facilitate the development of countermeasures against flavivirus outbreaks in the future.

A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition.

Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.

High-Contrast Bidirectional Optoelectronic Synapses based on 2D Molecular Crystal Heterojunctions for Motion Detection.

Light-stimulated optoelectronic synaptic devices are fundamental compositions of the neuromorphic vision system. However, there are still huge challenges to achieving both bidirectional synaptic behaviors under light stimuli and high performance. Herein, a bilayer 2D molecular crystal (2DMC) p-n heterojunction is developed to achieve high-performance bidirectional synaptic behaviors. The 2DMC heterojunction-based field effect transistor (FET) devices exhibit typical ambipolar properties and remarkable responsivity (R) of 3.58×104 A W-1 under weak light as low as 0.008 mW cm-2 . Excitatory and inhibitory synaptic behaviors are successfully realized by the same light stimuli under different gate voltages. Moreover, a superior contrast ratio (CR) of 1.53×103 is demonstrated by the ultrathin and high-quality 2DMC heterojunction, which transcends previous optoelectronic synapses and enables application for the motion detection of the pendulum. Furthermore, a motion detection network based on the device is developed to detect and recognize classic motion vehicles in road traffic with an accuracy exceeding 90%. This work provides an effective strategy for developing high-contrast bidirectional optoelectronic synapses and shows great potential in the intelligent bionic device and future artificial vision.

High-performance vertical field-effect organic photovoltaics.

Limited by the inherent energy loss (Eloss) in carrier transport process, the device efficiency of organic solar cells shows inferior to traditional inorganic photovoltaic devices. Generally, molecular design, morphology optimization and interfacial engineering are usually required to alleviate Eloss. Here, vertical field-effect organic photovoltaic (VFEOPV) by integrating an bulk-heterojunction (BHJ) organic photovoltaic (OPV) with vertical field effect transistor (VFET) is invented, in which VFET generates a large, uneven, internal electric field, eliminating the requirement for driving force to dissociate excitons and prevents non-radiative recombination in OPV. In this way, the performance of solar cell can be well controlled by the gate voltage of VFET and the Eloss of VFEOPVs based on J71: ITIC system is dramatically reduced below 0.2 eV, significantly improving power conversion efficiency (PCE) from 10% to 18% under gate voltage of 0.9 V, which only causes negligible additional power consumption (~10-4mJ/cm2). Besides, the device also exhibits multi-functionality including transistor and phototransistors with excellent photodector performance. This work provides a new and general strategy to improve the OPV performance which is compatible with present optimization methods, and can be applied to improve PCE of other types of solar cells such as Perovskite and inorganic solar cells.

Stretchable Transistor-Structured Artificial Synapses for Neuromorphic Electronics.

Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human-brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future.

Preparation and characterization of a neutralizing murine monoclonal antibody against tetanus toxin.

After Clostridium tetani infects the human body, it propagates under anaerobic conditions and produces tetanus neurotoxin (TeNT). TeNT can affect the central nervous system, inhibit the release of neurotransmitters, and result in respiratory failure, which are the root causes of death in tetanus patients. Identifying monoclonal antibodies (mAbs) targeting TeNT with neutralizing activity is urgently needed for the prevention and treatment of tetanus infection. In this study, through immunizing BALB/c mice with tetanus toxoid (TT), we obtained six positive hybridoma cell lines (1A7, 2C7, 3A7, 3H4, 4C1, and 4E12). Antibody isotyping showed that the antibodies are all of the IgG1/κ subclass. Ascites fluid was prepared by allogeneic ascites induction and the antibodies were purified through protein G affinity chromatography columns. Purities of the produced murine mAbs were all greater than 95%. All six antibodies bound to linear epitopes, among which 3A7 bound to the TeNT/L domain and the other five antibodies bound to the TeNT/Hc domain. Moreover, the affinity constants of these six antibodies against the antigen were all in the nanomolar range, and the affinity of 4E12 antibody reached the picomolar range. Results from toxin-neutralization assays in mice showed that 2C7 antibody delayed animal death, while 1A7, 3A7, 3H4, and 4E12 antibodies conferred partial protection. Additionally, 4C1 antibody offered complete protection, as 200 µg of 4C1 antibody fully protected against toxin challenge with 10 LD50 of TeNT and had a window period of 1 h. Antibody epitope grouping results revealed that the binding epitopes of 4C1 antibody were different from those of the other five antibodies. When 4C1 antibody was used in combination with another antibody, the neutralizing activities of antibodies were all evidently enhanced. Specifically, 4C1 combined with 3A7 antibody led to the greatest improvement in neutralizing activities, and 20 µg antibodies total (10 + 10 µg) fully protected against toxin challenge with 10 LD50. When 4E12, 3A7, and 4C1 antibodies were used in combination, 18 µg antibodies total (6 + 6 + 6 µg) completely neutralized 10 LD50 toxin. The present study derived murine mAbs with neutralizing activities and laid the foundation for follow-up therapeutic drug development for TeNT poisoning as well as establishment of TeNT detection methods.

Ultralow-Power Vertical Transistors for Multilevel Decoding Modes.

Organic field-effect transistors with parallel transmission and learning functions are of interest in the development of brain-inspired neuromorphic computing. However, the poor performance and high power consumption are the two main issues limiting their practical applications. Herein, an ultralow-power vertical transistor is demonstrated based on transition-metal carbides/nitrides (MXene) and organic single crystal. The transistor exhibits a high JON of 16.6 mA cm-2 and a high JON /JOFF ratio of 9.12 × 105 under an ultralow working voltage of -1 mV. Furthermore, it can successfully simulate the functions of biological synapse under electrical modulation along with consuming only 8.7 aJ of power per spike. It also permits multilevel information decoding modes with a significant gap between the readable time of professionals and nonprofessionals, producing a high signal-to-noise ratio up to 114.15 dB. This work encourages the use of vertical transistors and organic single crystal in decoding information and advances the development of low-power neuromorphic systems.

Self-powered high-sensitivity all-in-one vertical tribo-transistor device for multi-sensing-memory-computing.

Devices with sensing-memory-computing capability for the detection, recognition and memorization of real time sensory information could simplify data conversion, transmission, storage, and operations between different blocks in conventional chips, which are invaluable and sought-after to offer critical benefits of accomplishing diverse functions, simple design, and efficient computing simultaneously in the internet of things (IOT) era. Here, we develop a self-powered vertical tribo-transistor (VTT) based on MXenes for multi-sensing-memory-computing function and multi-task emotion recognition, which integrates triboelectric nanogenerator (TENG) and transistor in a single device with the simple configuration of vertical organic field effect transistor (VOFET). The tribo-potential is found to be able to tune ionic migration in insulating layer and Schottky barrier height at the MXene/semiconductor interface, and thus modulate the conductive channel between MXene and drain electrode. Meanwhile, the sensing sensitivity can be significantly improved by 711 times over the single TENG device, and the VTT exhibits excellent multi-sensing-memory-computing function. Importantly, based on this function, the multi-sensing integration and multi-model emotion recognition are constructed, which improves the emotion recognition accuracy up to 94.05% with reliability. This simple structure and self-powered VTT device exhibits high sensitivity, high efficiency and high accuracy, which provides application prospects in future human-mechanical interaction, IOT and high-level intelligence.

Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification.

Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics.

Ensemble learning-based feature selection for phosphorylation site detection.

SARS-COV-2 is prevalent all over the world, causing more than six million deaths and seriously affecting human health. At present, there is no specific drug against SARS-COV-2. Protein phosphorylation is an important way to understand the mechanism of SARS -COV-2 infection. It is often expensive and time-consuming to identify phosphorylation sites with specific modified residues through experiments. A method that uses machine learning to make predictions about them is proposed. As all the methods of extracting protein sequence features are knowledge-driven, these features may not be effective for detecting phosphorylation sites without a complete understanding of the mechanism of protein. Moreover, redundant features also have a great impact on the fitting degree of the model. To solve these problems, we propose a feature selection method based on ensemble learning, which firstly extracts protein sequence features based on knowledge, then quantifies the importance score of each feature based on data, and finally uses the subset of important features as the final features to predict phosphorylation sites.