Anisotropic Te/PdSe2 Van Der Waals Heterojunction for Self-Powered Broadband and Polarization-Sensitive Photodetection.

Polarization-sensitive broadband optoelectronic detection is crucial for future sensing, imaging, and communication technologies. Narrow bandgap 2D materials, such as Te and PdSe2, show promise for these applications, yet their polarization performance is limited by inherent structural anisotropies. In this work, a self-powered, broadband photodetector utilizing a Te/PdSe2 van der Waals (vdWs) heterojunction, with orientations meticulously tailored is introduced through polarized Raman optical spectra and tensor calculations to enhance linear polarization sensitivity. The device exhibits anisotropy ratios of 1.48 at 405 nm, 3.56 at 1550 nm, and 1.62 at 4 µm, surpassing previously-reported photodetectors based on pristine Te and PdSe2. Additionally, it exhibits high responsivity (617 mA W-1 at 1550 nm), specific detectivity (5.27 × 1010 Jones), fast response (≈4.5 µs), and an extended spectral range beyond 4 µm. The findings highlight the significance of orientation-engineered heterostructures in enhancing polarization-sensitive photodetectors and advancing optoelectronic technology.

Ultrasensitive Near-Infrared Polarization Photodetectors with Violet Phosphorus/InSe van der Waals Heterostructures.

Near-infrared (NIR) polarization photodetectors with two-dimensional (2D) semiconductors and their van der Waals (vdW) heterostructures have presented great impact for the development of a wide range of technologies, such as in the optoelectronics and communication fields. Nevertheless, the lack of a photogenerated charge carrier at the device's interface leads to a poor charge carrier collection efficiency and a low linear dichroism ratio, hindering the achievement of high-performance optoelectronic devices with multifunctionalities. Herein, we present a type-II violet phosphorus (VP)/InSe vdW heterostructure that is predicted via density functional theory calculation and confirmed by Kelvin probe force microscopy. Benefiting from the type-II band alignment, the VP/InSe vdW heterostructure-based photodetector achieves excellent photodetection performance such as a responsivity (R) of 182.8 A/W, a detectivity (D*) of 7.86 × 1012 Jones, and an external quantum efficiency (EQE) of 11,939% under a 1064 nm photon excitation. Furthermore, the photodetection performance can be enhanced by manipulating the device geometry by inserting a few layers of graphene between the VP and InSe (VP/Gr/InSe). Remarkably, the VP/Gr/InSe vdW heterostructure shows a competitive polarization sensitivity of 2.59 at 1064 nm and can be integrated as an image sensor. This work demonstrates that VP/InSe and VP/Gr/InSe vdW heterostructures will be effective for promising integrated NIR optoelectronics.

Two-dimensional topological semimetals: an emerging candidate for terahertz detectors and on-chip integration.

The importance of terahertz (THz) detection lies in its ability to provide detailed information in a non-destructive manner, making it a valuable tool across various domains including spectroscopy, communication, and security. The ongoing development of THz detectors aims to enhance their sensitivity, resolution and integration into compact and portable devices such as handheld scanners or integrated communication chips. Generally, two-dimensional (2D) materials are considered potential candidates for device miniaturization but detecting THz radiation using 2D semiconductors is generally difficult due to the ultra-small photon energy. However, this challenge is being addressed by the advent of topological semimetals (TSM) with zero-bandgap characteristics. These semimetals offer low-energy excitations in proximity to the Dirac point, which is particularly important for applications requiring a broad detection range. Their distinctive band structures with linear energy-momentum dispersion near the Fermi level also lead to high electron mobility and low effective mass. The presence of topologically protected dissipationless conducting channels and self-powered response provides a basis for low-energy integration. In order to establish paradigms for semimetal-based THz detectors, this review initially offers an analytical summary of THz detection principles. Then, the review demonstrates the distinct design of devices, the excellent performance derived from the topological surface state and unique band structures in TSM. Finally, we outline the prospective avenues for on-chip integration of TSM-based THz detectors. We believe this review can promote further research on the new generation of THz detectors and facilitate advancements in THz imaging, spectroscopy, and communication systems.

An improved Deeplab V3+ network based coconut CT image segmentation method.

Due to the unique structure of coconuts, their cultivation heavily relies on manual experience, making it difficult to accurately and timely observe their internal characteristics. This limitation severely hinders the optimization of coconut breeding. To address this issue, we propose a new model based on the improved architecture of Deeplab V3+. We replace the original ASPP(Atrous Spatial Pyramid Pooling) structure with a dense atrous spatial pyramid pooling module and introduce CBAM(Convolutional Block Attention Module). This approach resolves the issue of information loss due to sparse sampling and effectively captures global features. Additionally, we embed a RRM(residual refinement module) after the output level of the decoder to optimize boundary information between organs. Multiple model comparisons and ablation experiments are conducted, demonstrating that the improved segmentation algorithm achieves higher accuracy when dealing with diverse coconut organ CT(Computed Tomography) images. Our work provides a new solution for accurately segmenting internal coconut organs, which facilitates scientific decision-making for coconut researchers at different stages of growth.

Alternating BiI3-BiI van der Waals Photodetector with Low Dark Current and High-Performance Photodetection.

The emerging two-dimensional (2D) van der Waals (vdW) materials and their heterostructures hold great promise for optoelectronics and photonic applications beyond strictly lattice-matching constraints and grade interfaces. However, previous photodetectors and optoelectronic devices rely on relatively simple vdW heterostructures with one or two blocks. The realization of high-order heterostructures has been exponentially challenging due to conventional layer-by-layer arduous restacking or sequential synthesis. In this study, we present an approach involving the direct exfoliation of high-quality BiI3-BiI heterostructure nanosheets with alternating blocks, derived from solution-grown binary heterocrystals. These heterostructure-based photodetectors offer several notable advantages. Leveraging the "active layer energetics" of BiI layers and the establishment of a significant depletion region, our photodetector demonstrates a significant reduction in dark current compared with pure BiI3 devices. Specifically, the photodetector achieves an extraordinarily low dark current (<9.2 × 10-14 A at 5 V bias voltage), an impressive detectivity of 8.8 × 1012 Jones at 638 nm, and a rapid response time of 3.82 µs. These characteristics surpass the performance of other metal-semiconductor-metal (MSM) photodetectors based on various 2D materials and structures at visible wavelengths. Moreover, our heterostructure exhibits a broad-band photoresponse, covering the visible, near-infrared (NIR)-I, and NIR-II regions. In addition to these promising results, our heterostructure also demonstrated the potential for flexible and imaging applications. Overall, our study highlights the potential of alternating vdW heterostructures for future optoelectronics with low power consumption, fast response, and flexible requirements.

Nucleus accumbens deep brain stimulation improves depressive-like behaviors through BDNF-mediated alterations in brain functional connectivity of dopaminergic pathway.

Major depressive disorder (MDD), a common psychiatric condition, adversely affects patients' moods and quality of life. Despite the development of various treatments, many patients with MDD remain vulnerable and inadequately controlled. Since anhedonia is a feature of depression and there is evidence of leading to metabolic disorder, deep brain stimulation (DBS) to the nucleus accumbens (NAc) might be promising in modulating the dopaminergic pathway. To determine whether NAc-DBS alters glucose metabolism via mitochondrial alteration and neurogenesis and whether these changes increase neural plasticity that improves behavioral functions in a chronic social defeat stress (CSDS) mouse model. The Lab-designed MR-compatible neural probes were implanted in the bilateral NAc of C57BL/6 mice with and without CSDS, followed by DBS or sham stimulation. All animals underwent open-field and sucrose preference testing, and brain resting-state functional MRI analysis. Meanwhile, we checked the placement of neural probes in each mouse by T2 images. By confirming the placement location, mice with incorrect probe placement (the negative control group) showed no significant therapeutic effects in behavioral performance and functional connectivity (FC) after receiving electrical stimulation and were excluded from further analysis. Western blotting, seahorse metabolic analysis, and electron microscopy were further applied for the investigation of NAc-DBS. We found NAc-DBS restored emotional deficits in CSDS-subjected mice. Concurrent with behavioral amelioration, the CSDS DBS-on group exhibited enhanced FC in the dopaminergic pathway with increased expression of BDNF- and NeuN-positive cells increased dopamine D1 receptor, dopamine D2 receptors, and TH in the medial prefrontal cortex, NAc, ventral hippocampus, ventral tegmental area, and amygdala. Increased pAMPK/total AMPK and PGC-1α levels, functions of oxidative phosphorylation, and mitochondrial biogenesis were also observed after NAc-DBS treatment. Our findings demonstrate that NAc-DBS can promote BDNF expression, which alters FC and metabolic profile in the dopaminergic pathway, suggesting a potential strategy for ameliorating emotional processes in individuals with MDD.

Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task.

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

Performance management algorithm of financial shared service center based on Internet of Things public cloud privacy protection.

The Internet of Things (IoT) refers to the use of various communication technologies to achieve the interconnection of everything in cyberspace, and to achieve smart home and intelligent transportation, thus generating unprecedented amounts of data. In the financial sharing center, all businesses can extract effective data from these massive databases for analysis, and use data analysis tools to collect business, financial, human, process, knowledge and social data. At present, various types of IT (Internet Technology) systems have been widely used in financial sharing centers. However, a large number of sensitive data have also been generated. In order to protect these sensitive data, there is a high requirement for the personal information of IT system operation and financial sharing center personnel. In order to protect user data privacy, the optimal and most effective use of IT systems is an important issue that must be considered in privacy management. At present, there are many algorithms to protect data and privacy, but the effect is not ideal. Considering the balance between privacy issues, this paper proposed a K-means clustering algorithm based on IoT public cloud privacy protection technology to analyze the performance management of financial sharing center. The research results showed that before the improvement, the average number of employees who were dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 57.9 and 57.8% respectively, more than half of them. After the improvement of IoT based public cloud privacy protection, the average number of employees dissatisfied with the post training ability and information platform construction ability of the financial sharing center was 5 and 3.9%, far less than the data prior to the improvement. It showed that IoT public cloud privacy protection was conducive to the performance management of the financial sharing center, and the relationship between the two was positive.

Synthesis and Broadband Photodetection of a P-Type 1D Van der Waals Semiconductor HfSnS3.

1D van der Waals (vdW) materials have attracted significant interest in recent years due to their giant anisotropic and weak interlayer-coupled characters. More 1D vdW materials are urgently to be exploited for satisfying the practice requirement. Herein, the study of 1D vdW ternary HfSnS3 high-quality single crystals grown via the chemical vapor transport technique is reported. The Raman vibration modes and band structure of HfSnS3 are analyzed via DFT calculations. Its strong in-plane anisotropic is verified by the polarized Raman spectroscopy. The field-effect transistors (FETs) based on the HfSnS3 nanowires demonstrate p-type semiconducting behavior as well as outstanding photoresponse in a broadband range from UV to near-infrared (NIR) with short response times of ≈0.355 ms, high responsivity of ≈11.5 A W-1 , detectivity of ≈8.2 × 1011 , external quantum efficiency of 2739%, excellent environmental stability, and repeatability. Furthermore, a typical photoconductivity effect of the photodetector is illustrated. These comprehensive characteristics can promote the application of the p-type 1D vdW material HfSnS3 in optoelectronics.

Ultraviolet-Visible-Short-Wavelength Infrared Broadband and Fast-Response Photodetectors Enabled by Individual Monocrystalline Perovskite Nanoplate.

Perovskite-based photodetectors exhibit potential applications in communication, neuromorphic chips, and biomedical imaging due to their outstanding photoelectric properties and facile manufacturability. However, few of perovskite-based photodetectors focus on ultraviolet-visible-short-wavelength infrared (UV-Vis-SWIR) broadband photodetection because of the relatively large bandgap. Moreover, such broadband photodetectors with individual nanocrystal channel featuring monolithic integration with functional electronic/optical components have hardly been explored. Herein, an individual monocrystalline MAPbBr3 nanoplate-based photodetector is demonstrated that simultaneously achieves efficient UV-Vis-SWIR detection and fast-response. Nanoplate photodetectors (NPDs) are prepared by assembling single nanoplate on adjacent gold electrodes. NPDs exhibit high external quantum efficiency (EQE) and detectivity of 1200% and 5.37 × 1012 Jones, as well as fast response with rise time of 80 µs. Notably, NPDs simultaneously achieve high EQE and fast response, exceeding most perovskite devices with multi-nanocrystal channel. Benefiting from the high specific surface area of nanoplate with surface-trap-assisted absorption, NPDs achieve high performance in the near-infrared and SWIR spectral region of 850-1450 nm. Unencapsulated devices show outstanding UV-laser-irradiation endurance and decent periodicity and repeatability after 29-day-storage in atmospheric environment. Finally, imaging applications are demonstrated. This work verifies the potential of perovskite-based broadband photodetection, and stimulates the monolithic integration of various perovskite-based devices.

Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray.

BACKGROUND: As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established. RESULTS: The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called "CCID", which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit. CONCLUSION: The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts.

Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring.

Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were -1.36 ± 7.24 and -2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.

Solution-grown BiI/BiI3 van der Waals heterostructures for sensitive X-ray detection.

X-ray detectors must be operated at minimal doses to reduce radiation health risks during X-ray security examination or medical inspection, therefore requiring high sensitivity and low detection limits. Although organolead trihalide perovskites have rapidly emerged as promising candidates for X-ray detection due to their low cost and remarkable performance, these materials threaten the safety of the human body and environment due to the presence of lead. Here we present the realization of highly sensitive X-ray detectors based on an environmentally friendly solution-grown thick BiI/BiI3/BiI (BixIy) van der Waals heterostructure. The devices exhibit anisotropic X-ray detection response with a sensitivity up to 4.3 × 104 µC Gy-1 cm-2 and a detection limit as low as 34 nGy s-1. At the same time, our BixIy detectors demonstrate high environmental and hard radiation stabilities. Our work motivates the search for new van der Waals heterostructure classes to realize high-performance X-ray detectors and other optoelectronic devices without employing toxic elements.

Visualization and quantification of coconut using advanced computed tomography postprocessing technology.

INTRODUCTION: Computed tomography (CT) is a non-invasive examination tool that is widely used in medicine. In this study, we explored its value in visualizing and quantifying coconut. MATERIALS AND METHODS: Twelve coconuts were scanned using CT for three months. Axial CT images of the coconuts were obtained using a dual-source CT scanner. In postprocessing process, various three-dimensional models were created by volume rendering (VR), and the plane sections of different angles were obtained through multiplanar reformation (MPR). The morphological parameters and the CT values of the exocarp, mesocarp, endocarp, embryo, bud, solid endosperm, liquid endosperm, and coconut apple were measured. The analysis of variances was used for temporal repeated measures and linear and non-linear regressions were used to analyze the relationship between the data. RESULTS: The MPR images and VR models provide excellent visualization of the different structures of the coconut. The statistical results showed that the weight of coconut and liquid endosperm volume decreased significantly during the three months, while the CT value of coconut apple decreased slightly. We observed a complete germination of a coconut, its data showed a significant negative correlation between the CT value of the bud and the liquid endosperm volume (y = -2.6955x + 244.91; R2 = 0.9859), and a strong positive correlation between the height and CT value of the bud (y = 1.9576 ln(x) -2.1655; R2 = 0.9691). CONCLUSION: CT technology can be used for visualization and quantitative analysis of the internal structure of the coconut, and some morphological changes and composition changes of the coconut during the germination process were observed during the three-month experiment. Therefore, CT is a potential tool for analyzing coconuts.

Neuro-Inspired Reinforcement Learning to Improve Trajectory Prediction in Reward-Guided Behavior.

Hippocampal pyramidal cells and interneurons play a key role in spatial navigation. In goal-directed behavior associated with rewards, the spatial firing pattern of pyramidal cells is modulated by the animal's moving direction toward a reward, with a dependence on auditory, olfactory, and somatosensory stimuli for head orientation. Additionally, interneurons in the CA1 region of the hippocampus monosynaptically connected to CA1 pyramidal cells are modulated by a complex set of interacting brain regions related to reward and recall. The computational method of reinforcement learning (RL) has been widely used to investigate spatial navigation, which in turn has been increasingly used to study rodent learning associated with the reward. The rewards in RL are used for discovering a desired behavior through the integration of two streams of neural activity: trial-and-error interactions with the external environment to achieve a goal, and the intrinsic motivation primarily driven by brain reward system to accelerate learning. Recognizing the potential benefit of the neural representation of this reward design for novel RL architectures, we propose a RL algorithm based on [Formula: see text]-learning with a perspective on biomimetics (neuro-inspired RL) to decode rodent movement trajectories. The reward function, inspired by the neuronal information processing uncovered in the hippocampus, combines the preferred direction of pyramidal cell firing as the extrinsic reward signal with the coupling between pyramidal cell-interneuron pairs as the intrinsic reward signal. Our experimental results demonstrate that the neuro-inspired RL, with a combined use of extrinsic and intrinsic rewards, outperforms other spatial decoding algorithms, including RL methods that use a single reward function. The new RL algorithm could help accelerate learning convergence rates and improve the prediction accuracy for moving trajectories.

Wafer-Scale Synthesis of 2D Dirac Heterostructures for Self-Driven, Fast, Broadband Photodetectors.

Type-II Dirac semimetal platinum ditelluride (PtTe2) is a promising functional material for photodetectors because of its specially tilted Dirac cones, strong light absorption, and high carrier mobilities. The stack of two-dimensional (2D) Dirac heterostructures consisting of PtTe2 and graphene could overcome the limit of detection range and response time occurring in the heterostructures of graphene and other low-mobility and large-gap transition metal dichalcogenides (TMDs). Here, we report an approach for achieving highly controllable, wafer-scale production of 2D Dirac heterostructures of PtTe2/graphene with tunable thickness, variable size, and CMOS compatibility. More importantly, the optimized recipes achieve the exact stoichiometric ratio of 1:2 for Pt and Te elements without contaminating the underlayer graphene film. Because of the built-in electric field at the junction area, the photodetectors based on the PtTe2/graphene heterostructure are self-driven with a broadband photodetection from 405 to 1850 nm. In particular, the photodetectors have a high responsivity of up to ∼0.52 AW-1 (without bias) and a fast response time of ∼8.4 µs. Our work demonstrated an approach to synthesizing hybrid 2D Dirac heterostructures, which can be applied in the integration of on-chip, CMOS-compatible photodetectors with near-infrared detection, high sensitivity, and low energy consumption.

A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control.

An exoskeleton, a wearable device, was designed based on the user's physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between the subject and exoskeleton. Here, we propose an EMG-based single-joint exoskeleton system by merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models regarding the rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported the effective and accurate performance of an exoskeleton controlled by EMG signals.

Emerging Two-Dimensional Tellurene and Tellurides for Broadband Photodetectors.

As with all stylish 2D functional materials, tellurene and tellurides possessing excellent physical and chemical properties such as high environmental stability, tunable narrow bandgap, and lower thermal conductivity, have aroused the great interest of the researchers. These properties of such materials also form the basis for relatively newfangled scholarly fields involving advanced topics, especially for broadband photodetectors. Integrating the excellent properties of many 2D materials, tellurene/telluride-based photodetectors show great flexibility, higher frequency response or faster time response, high signal-to-noise ratio, and so on, which make them leading the frontier of photodetector research. To fully understand the excellent properties of tellurene/tellurides and their optoelectronic applications, the recent advances in tellurene/telluride-based photodetectors are maximally summarized. Benefiting from the solid research in this field, the challenges and opportunities of tellurene/tellurides for future optoelectronic applications are also discussed in this review, which might provide possibilities for the realization of state-of-the-art high-performance tellurene/telluride-based devices.

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease.

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Flexible 2D Materials beyond Graphene: Synthesis, Properties, and Applications.

2D materials are now at the forefront of state-of-the-art nanotechnologies due to their fascinating properties and unique structures. As expected, low-cost, high-volume, and high-quality 2D materials play an important role in the applications of flexible devices. Although considerable progress has been achieved in the integration of a series of novel 2D materials beyond graphene into flexible devices, a lot remains to be known. At this stage of their development, the key issues concern how to make further improvements to high-performance and scalable-production. Herein, recent progress in the quest to improve the current state of the art for 2D materials beyond graphene is reviewed. Namely, the properties and synthesis techniques of 2D materials are first introduced. Then, both the advantages and challenges of these 2D materials for flexible devices are also highlighted. Finally, important directions for future advancements toward efficient, low-cost, and stable flexible devices are outlined.