Vertical organic electrochemical transistors for complementary circuits.

Organic electrochemical transistors (OECTs) and OECT-based circuitry offer great potential in bioelectronics, wearable electronics and artificial neuromorphic electronics because of their exceptionally low driving voltages (<1 V), low power consumption (<1 µW), high transconductances (>10 mS) and biocompatibility1-5. However, the successful realization of critical complementary logic OECTs is currently limited by temporal and/or operational instability, slow redox processes and/or switching, incompatibility with high-density monolithic integration and inferior n-type OECT performance6-8. Here we demonstrate p- and n-type vertical OECTs with balanced and ultra-high performance by blending redox-active semiconducting polymers with a redox-inactive photocurable and/or photopatternable polymer to form an ion-permeable semiconducting channel, implemented in a simple, scalable vertical architecture that has a dense, impermeable top contact. Footprint current densities exceeding 1 kA cm-2 at less than ±0.7 V, transconductances of 0.2-0.4 S, short transient times of less than 1 ms and ultra-stable switching (>50,000 cycles) are achieved in, to our knowledge, the first vertically stacked complementary vertical OECT logic circuits. This architecture opens many possibilities for fundamental studies of organic semiconductor redox chemistry and physics in nanoscopically confined spaces, without macroscopic electrolyte contact, as well as wearable and implantable device applications.

High-performance and low-power source-gated transistors enabled by a solution-processed metal oxide homojunction.

Cost-effective fabrication of mechanically flexible low-power electronics is important for emerging applications including wearable electronics, artificial intelligence, and the Internet of Things. Here, solution-processed source-gated transistors (SGTs) with an unprecedented intrinsic gain of ~2,000, low saturation voltage of +0.8 ± 0.1 V, and a ~25.6 µW power consumption are realized using an indium oxide In2O3/In2O3:polyethylenimine (PEI) blend homojunction with Au contacts on Si/SiO2. Kelvin probe force microscopy confirms source-controlled operation of the SGT and reveals that PEI doping leads to more effective depletion of the reverse-biased Schottky contact source region. Furthermore, using a fluoride-doped AlOx gate dielectric, rigid (on a Si substrate) and flexible (on a polyimide substrate) SGTs were fabricated. These devices exhibit a low driving voltage of +2 V and power consumption of ~11.5 µW, yielding inverters with an outstanding voltage gain of >5,000. Furthermore, electrooculographic (EOG) signal monitoring can now be demonstrated using an SGT inverter, where a ~1.0 mV EOG signal is amplified to over 300 mV, indicating significant potential for applications in wearable medical sensing and human-computer interfacing.

Stretchable Electronics with Strain-Resistive Performance.

Stretchable electronics have attracted tremendous attention amongst academic and industrial communities due to their prospective applications in personal healthcare, human-activity monitoring, artificial skins, wearable displays, human-machine interfaces, etc. Other than mechanical robustness, stable performances under complex strains in these devices that are not for strain sensing are equally important for practical applications. Here, a comprehensive summarization of recent advances in stretchable electronics with strain-resistive performance is presented. First, detailed overviews of intrinsically strain-resistive stretchable materials, including conductors, semiconductors, and insulators, are given. Then, systematic representations of advanced structures, including helical, serpentine, meshy, wrinkled, and kirigami-based structures, for strain-resistive performance are summarized. Next, stretchable arrays and circuits with strain-resistive performance, that integrate multiple functionalities and enable complex behaviors, are introduced. This review presents a detailed overview of recent progress in stretchable electronics with strain-resistive performances and provides a guideline for the future development of stretchable electronics.

Gender differences in 381 patients undergoing isolated mitral regurgitation repair.

Objectives To compare the gender differences in isolated mitral regurgitation (MR) repair. Methods Of 381 adults aged 54.8±12.3 years undergoing mitral valve repair (MVP) for isolated MR from 01/2019-12/2022, the baseline and operative data, and outcomes were compared between 161 women (42.3%) and 220 men (57.7%). Results Women tended to be non-smoker (98.1% vs 45%, P<0.001), and have more cerebrovascular accidents (38.5% vs 24.1%, P=0.004), lower creatinine (70.0±19.5 vs 86.3±19.9 µmol/dL, P<0.001), smaller LVEDD (54.4±6.7 vs 57.8±6.6 mm, P<0.001) and isolated annular dilatation (19.3% vs 9.1%, P=0.010). One female died of stroke at 2 days (0.3%). Another female (0.3%) underwent MV replacement for failed repair. Stroke occurred in 4 (1.0%). Two underwent re-exploration for bleeding (0.5%). Women were more likely to have less 24-hour drainage (290±143 vs 385±196 mL, P<0.001). Over a mean follow-up of 2.1±1.1 years (100% complete), one woman died, one man underwent a reoperation; 28 had moderate MR, and 9 had severe MR. Neither did early and late mortality and reoperation, nor freedom from late moderate/severe MR (71.6% vs 71.4% at 5 years; P=0.992) differ significantly between two genders. Predictors for late moderate/severe MR were anterior leaflet prolapse (hazard ratio [HR] 4.45; 95% confidence interval [CI] 1.18-16.72; P=0.027) and isolated annular dilation (HR 5.47, 95% CI 1.29-23.25; P=0.021). Conclusions Despite significant differences in smoking, cerebrovascular accidents, creatinine, LVEDD, and isolated annular dilatation at baseline, and 24-hour drainage, women and men did not show significant difference in early and late survival, reoperation and freedom from late moderate/severe MR.

Optimized Design of EdgeBoard Intelligent Vehicle Based on PP-YOLOE.

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system's ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles' operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model's performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms.

Highly stretchable organic electrochemical transistors with strain-resistant performance.

Realizing fully stretchable electronic materials is central to advancing new types of mechanically agile and skin-integrable optoelectronic device technologies. Here we demonstrate a materials design concept combining an organic semiconductor film with a honeycomb porous structure with biaxially prestretched platform that enables high-performance organic electrochemical transistors with a charge transport stability over 30-140% tensional strain, limited only by metal contact fatigue. The prestretched honeycomb semiconductor channel of donor-acceptor polymer poly(2,5-bis(2-octyldodecyl)-3,6-di(thiophen-2-yl)-2,5-diketo-pyrrolopyrrole-alt-2,5-bis(3-triethyleneglycoloxy-thiophen-2-yl) exhibits high ion uptake and completely stable electrochemical and mechanical properties over 1,500 redox cycles with 104 stretching cycles under 30% strain. Invariant electrocardiogram recording cycles and synapse responses under varying strains, along with mechanical finite element analysis, underscore that the present stretchable organic electrochemical transistor design strategy is suitable for diverse applications requiring stable signal output under deformation with low power dissipation and mechanical robustness.

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network.

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent's attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the "feature store & return" module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

Flexible Neural Probes with Electrochemical Modified Microelectrodes for Artifact-Free Optogenetic Applications.

With the rapid increase in the use of optogenetics to investigate nervous systems, there is high demand for neural interfaces that can simultaneously perform optical stimulation and electrophysiological recording. However, high-magnitude stimulation artifacts have prevented experiments from being conducted at a desirably high temporal resolution. Here, a flexible polyimide-based neural probe with polyethylene glycol (PEG) packaged optical fiber and Pt-Black/PEDOT-GO (graphene oxide doped poly(3,4-ethylene-dioxythiophene)) modified microelectrodes was developed to reduce the stimulation artifacts that are induced by photoelectrochemical (PEC) and photovoltaic (PV) effects. The advantages of this design include quick and accurate implantation and high-resolution recording capacities. Firstly, electrochemical performance of the modified microelectrodes is significantly improved due to the large specific surface area of the GO layer. Secondly, good mechanical and electrochemical stability of the modified microelectrodes is obtained by using Pt-Black as bonding layer. Lastly, bench noise recordings revealed that PEC noise amplitude of the modified neural probes could be reduced to less than 50 µV and no PV noise was detected when compared to silicon-based neural probes. The results indicate that this device is a promising optogenetic tool for studying local neural circuits.

Adaptive adjacent context negotiation network for object detection in remote sensing imagery.

Accurate localization of objects of interest in remote sensing images (RSIs) is of great significance for object identification, resource management, decision-making and disaster relief response. However, many difficulties, like complex backgrounds, dense target quantities, large-scale variations, and small-scale objects, which make the detection accuracy unsatisfactory. To improve the detection accuracy, we propose an Adaptive Adjacent Context Negotiation Network (A2CN-Net). Firstly, the composite fast Fourier convolution (CFFC) module is given to reduce the information loss of small objects, which is inserted into the backbone network to obtain spectral global context information. Then, the Global Context Information Enhancement (GCIE) module is given to capture and aggregate global spatial features, which is beneficial for locating objects of different scales. Furthermore, to alleviate the aliasing effect caused by the fusion of adjacent feature layers, a novel Adaptive Adjacent Context Negotiation network (A2CN) is given to adaptive integration of multi-level features, which consists of local and adjacent branches, with the local branch adaptively highlighting feature information and the adjacent branch introducing global information at the adjacent level to enhance feature representation. In the meantime, considering the variability in the focus of feature layers in different dimensions, learnable weights are applied to the local and adjacent branches for adaptive feature fusion. Finally, extensive experiments are performed in several available public datasets, including DIOR and DOTA-v1.0. Experimental studies show that A2CN-Net can significantly boost detection performance, with mAP increasing to 74.2% and 79.2%, respectively.

Ternary Logic Circuit and Neural Network Integration via Small Molecule-Based Antiambipolar Vertical Electrochemical Transistor.

Circuits based on organic electrochemical transistors (OECTs) have great potential in the fields of biosensors and artificial neural computation due to their biocompatibility and neural similarity. However, the integration of OECT-based circuits lags far behind other emerging electronics. Here, ternary inverters based on antiambipolar vertical OECTs (vOECTs) and their integration with the establishment of neural networks are demonstrated. Specifically, by adopting a small molecule (t-gdiPDI) as the channel of vOECT, high antiambipolar performance, with current density of 33.9 ± 2.1 A cm-2 under drain voltage of 0.1 V, peak voltage ≈0 V, low driving voltage < ± 0.6 V, and current on/off ratio > 106, are realized. Consequently, vertically stacked ternary circuits based solely on OECTs are constructed for the first time, showing three distinct logical states and high integration density. By further developing inverter array as the internal fundamental units of ternary weight network hardware circuits for ternary processing and computation, it demonstrates excellent data classification and recognition capabilities. This work demonstrates the possibility of constructing multi-valued logic circuits by OECTs and promotes a new strategy for high-density integration and multivalued computing systems based on organic circuits.

Multi-wave and hybrid imaging techniques: a new direction for nondestructive testing and structural health monitoring.

In this article, the state-of-the-art multi-wave and hybrid imaging techniques in the field of nondestructive evaluation and structural health monitoring were comprehensively reviewed. A new direction for assessment and health monitoring of various structures by capitalizing the advantages of those imaging methods was discussed. Although sharing similar system configurations, the imaging physics and principles of multi-wave phenomena and hybrid imaging methods are inherently different. After a brief introduction of nondestructive evaluation (NDE) , structure health monitoring (SHM) and their related challenges, several recent advances that have significantly extended imaging methods from laboratory development into practical applications were summarized, followed by conclusions and discussion on future directions.

Distributed Recursive Filtering Over Sensor Networks Under Random Access Protocol: When State Saturation Meets Censored Measurement.

In this article, a new distributed filtering problem is studied for a class of state-saturated time-varying systems over sensor networks under measurement censoring, where the censored measurements are described by the Tobit measurement model. To curb the data collision and ease communication burden, a random access protocol (RAP) is implemented onto the sensor-to-filter channels to orchestrate the transmission sequence of multiple sensor nodes. The purpose of the addressed problem is to construct a state-saturated distributed filter such that upper bounds (on filtering error covariances) are guaranteed and filter parameters are determined to accommodate both measurement censoring and state saturation under the RAP. By means of matrix difference equations, the desired upper bounds are first acquired and later minimized through appropriately designing filter parameters. Particularly, the sparsity issue with respect to the network topology is tackled via the employing certain matrix simplification technique. A simulation example is finally presented to showcase the applicability of the proposed state-saturated distributed filtering algorithm.

Flexible and Stretchable Organic Electrochemical Transistors for Physiological Sensing Devices.

Flexible and stretchable bioelectronics provides a biocompatible interface between electronics and biological systems and has received tremendous attention for in situ monitoring of various biological systems. Considerable progress in organic electronics has made organic semiconductors, as well as other organic electronic materials, ideal candidates for developing wearable, implantable, and biocompatible electronic circuits due to their potential mechanical compliance and biocompatibility. Organic electrochemical transistors (OECTs), as an emerging class of organic electronic building blocks, exhibit significant advantages in biological sensing due to the ionic nature at the basis of the switching behavior, low driving voltage (<1 V), and high transconductance (in millisiemens range). During the past few years, significant progress in constructing flexible/stretchable OECTs (FSOECTs) for both biochemical and bioelectrical sensors has been reported. In this regard, to summarize major research accomplishments in this emerging field, this review first discusses structure and critical features of FSOECTs, including working principles, materials, and architectural engineering. Next, a wide spectrum of relevant physiological sensing applications, where FSOECTs are the key components, are summarized. Last, major challenges and opportunities for further advancing FSOECT physiological sensors are discussed.

Integrated opposite charge grafting induced ionic-junction fiber.

The emergence of ionic-junction devices has attracted growing interests due to the potential of serving as signal transmission and translation media between electronic devices and biological systems using ions. Among them, fiber-shaped iontronics possesses a great advantage in implantable applications owing to the unique one-dimensional geometry. However, fabricating stable ionic-junction on curved surfaces remains a challenge. Here, we developed a polyelectrolyte based ionic-junction fiber via an integrated opposite charge grafting method capable of large-scale continuous fabrication. The ionic-junction fibers can be integrated into functions such as ionic diodes and ionic bipolar junction transistors, where rectification and switching of input signals are implemented. Moreover, synaptic functionality has also been demonstrated by utilizing the fiber memory capacitance. The connection between the ionic-junction fiber and sciatic nerves of the mouse simulating end-to-side anastomosis is further performed to realize effective nerve signal conduction, verifying the capability for next-generation artificial neural pathways in implantable bioelectronics.

Seeking Tracking Consensus for General Linear Multiagent Systems With Fixed and Switching Signed Networks.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

Asynchronous Tracking Control of Leader-Follower Multiagent Systems With Input Uncertainties Over Switching Signed Digraphs.

Signed digraphs with both positive and negative weighted edges are widely applied to explain cooperative and competitive interactions arising from various social, biological, and physical systems. This article formulates and solves the asynchronous tracking control problem of multiagent systems with input uncertainties on switching signed digraphs. In the interaction setting, we assume that the leader moves at a time-varying acceleration that cannot be measured by the followers accurately, and further suppose that each agent receives its neighbors' states information at certain instants determined by its own clock, which is not necessary to be synchronized with those of other agents. Using dynamically changing spanning subdigraphs of signed digraphs to describe graphically asynchronous interactions, the asynchronous tracking problem is equivalently transformed into a convergence problem of products of general substochastic matrices (PGSSM), in which the matrix elements are not necessarily non-negative and the row sums are less than or equal to 1. With the help of the matrix analysis technique and the composition of binary relations, we propose a new and original method to deal with the convergence problem of PGSSM, and further establish a spanning tree condition for asynchronous tracking control. Finally, the validity of the theoretical findings is verified through several numerical examples.

Electrochemical biosensors based on saliva electrolytes for rapid detection and diagnosis.

In recent years, electrochemical biosensors (ECBSs) have shown significant potential for real-time disease diagnosis and in situ physical condition monitoring. As a multi-constituent oral fluid comprising various disease signaling biomarkers, saliva has drawn much attention in the field of point-of-care (POC) testing. In particular, during the outbreak of the COVID-19 pandemic, ECBSs which hold the simplicity of a single-step assay compared with the multi-step assay of traditional testing methods are expected to relieve the human and economic burden caused by the massive and long-term sample testing process. Noteworthily, ECBSs for the detection of SARS-CoV-2 in saliva have already been developed and may replace current testing methods. Furthermore, the detection scope has expanded from routine indices such as sugar and uric acid to abnormal biomarkers for early-stage disease detection and drug level monitoring, which further facilitated the evolution of ECBSs in the last 5 years. This review is divided into several main sections. First, we discussed the latest advancements and representative research on ECBSs for saliva testing. Then, we focused on a novel kind of ECBS, organic electrochemical transistors (OECTs), which hold great advantages of high sensitivity and signal-to-noise ratio and on-site detection. Finally, application of ECBSs with integrated portable platforms in oral cavities, which lead to powerful auxiliary testing means for telemedicine, has also been discussed.

Asynchronous Frequency-Dependent Fault Detection for Nonlinear Markov Jump Systems Under Wireless Fading Channels.

In this article, the asynchronous fault detection (FD) strategy is investigated in frequency domain for nonlinear Markov jump systems under fading channels. In order to estimate the system dynamics and meet the fact that not all the running modes can be observed exactly, a set of asynchronous FD filters is proposed. By using statistical methods and the Lynapunov stability theory, the augmented system is shown to be stochastic stable with a prescribed l2 gain even under fading transmissions. Then, a novel lemma is developed to capture the finite frequency performance. Some solvable conditions with less conservatism are subsequently deduced by exploiting novel decoupling techniques and additional slack variables. Besides, the FD filter gains could be calculated with the aid of the derived conditions. Finally, the effectiveness of the proposed method is shown by an illustrative example.

Readily Accessible Metallic Micro-Island Arrays for High-Performance Metal Oxide Thin-Film Transistors.

Thin-film transistors using metal oxide semiconductors are essential in many unconventional electronic devices. Nevertheless, further advances will be necessary to broaden their technological appeal. Here, a new strategy is reported to achieve high-performance solution-processed metal oxide thin-film transistors (MOTFTs) by introducing a metallic micro-island array (M-MIA) on top of the MO back channel, where the MO is a-IGZO (amorphous indium-gallium-zinc-oxide). Here Al-MIAs are fabricated using honeycomb cinnamate cellulose films, created by a scalable breath-figure method, as a shadow mask. For IGZO TFTs, the electron mobility (µe ) increases from ≈3.6 cm2 V-1 s-1 to near 15.6 cm2 V-1 s-1 for optimal Al-MIA dimension/coverage of 1.25 µm/51%. The Al-MIA IGZO TFT performance is superior to that of controls using compact/planar Al layers (Al-PL TFTs) and Au-MIAs with the same channel coverage. Kelvin probe force microscopy and technology computer-aided design simulations reveal that charge transfer occurs between the Al and the IGZO channel which is optimized for specific Al-MIA dimensions/surface channel coverages. Furthermore, such Al-MIA IGZO TFTs with a high-k fluoride-doped alumina dielectric exhibit a maximum µe of >50.2 cm2 V-1 s-1 . This is the first demonstration of a micro-structured MO semiconductor heterojunction with submicrometer resolution metallic arrays for enhanced transistor performance and broad applicability to other devices.

Flexible Neural Probes with Optical Artifact-Suppressing Modification and Biofriendly Polypeptide Coating.

The advent of optogenetics provides a well-targeted tool to manipulate neurons because of its high time resolution and cell-type specificity. Recently, closed-loop neural manipulation techniques consisting of optical stimulation and electrical recording have been widely used. However, metal microelectrodes exposed to light radiation could generate photoelectric noise, thus causing loss or distortion of neural signal in recording channels. Meanwhile, the biocompatibility of neural probes remains to be improved. Here, five kinds of neural interface materials are deposited on flexible polyimide-based neural probes and illuminated with a series of blue laser pulses to study their electrochemical performance and photoelectric noises for single-unit recording. The results show that the modifications can not only improve the electrochemical performance, but can also reduce the photoelectric artifacts. In particular, the double-layer composite consisting of platinum-black and conductive polymer has the best comprehensive performance. Thus, a layer of polypeptide is deposited on the entire surface of the double-layer modified neural probes to further improve their biocompatibility. The results show that the biocompatible polypeptide coating has little effect on the electrochemical performance of the neural probe, and it may serve as a drug carrier due to its special micromorphology.