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The quantitative detection of circularly polarized light (CPL) is necessary in next-generation optical communication carrying high-density information and in phase-controlled displays exhibiting volumetric imaging. In the current technology, multiple pixels of different wavelengths and polarizers are required, inevitably resulting in high loss and low detection efficiency. Here, we demonstrate a highly efficient CPL-detecting transistor composed of chiral plasmonic nanoparticles with a high Khun’s dissymmetry (g-factor) of 0.2 and a high mobility conducting oxide of InGaZnO. The device successfully distinguished the circular polarization state and displayed an unprecedented photoresponsivity of over 1 A/W under visible CPL excitation. This observation is mainly attributed to the hot electron generation in chiral plasmonic nanoparticles and to the effective collection of hot electrons in the oxide semiconducting transistor. Such characteristics further contribute to opto-neuromorphic operation and the artificial nervous system based on the device successfully performs image classification work. We anticipate that our strategy will aid in the rational design and fabrication of a high-performance CPL detector and opto-neuromorphic operation with a chiral plasmonic structure depending on the wavelength and circular polarization state. One challenge with using circularly polarized light is an increased device complexity typically required to provide sufficient sensitivity to the polarization. Here, by integrating chiral plasmonic nanoparticles with a InGaZnO hot electron transistor, the authors present a polarization sensitive, optically active transistor.

SnO2 is a promising material for both Li-ion and Na-ion batteries owing to its high theoretical capacities. Unfortunately, the electrochemical performance of SnO2 is unsatisfactory because of the large volume change that occurs during cycling, low electronic conductivity of inactive oxide matrix, and poor kinetics, which are particularly severe in Na-ion batteries. Herein, ultra-fine SnO2 nanocrystals anchored on a unique three-dimensional （3D） porous reduced graphene oxide （rGO） matrix are described as promising bifunctional electrodes for Li-ion and Na-ion batteries with excellent rate capability and long cycle life. Ultra-fine SnO2 nanocrystals of size -6 nm are well-coordinated to the graphene sheets that comprise the 3D macro-porous structure. Notably, superior rate capability was obtained up to 3 C （1In C is a measure of the rate that allows the cell to be charged/discharged in n h） for both batteries. In situ X-ray diffractometry measurements during lithiation （or sodiation） and delithiation （or desodiation） were combined with various electrochemical techniques to reveal the real-time phase evolution. This critical information was linked with the internal resistance, ion diffusivity （DLi＋ and DNa＋）, and the unique structure of the composite electrode materials to explain their excellent electrochemical performance. The improved capacity and superior rate capabilities demonstrated in this work can be ascribed to the enhanced transport kinetics of both electrons and ions within the electrode structure because of the well-interconnected, 3D macro-porous rGO matrix. The porous rGO matrix appears to play a more important role in sodium-ion batteries （SIBs）, where the larger mass/radius of Na-ions are marked concerns.

The process of memory and learning in biological systems is multimodal, as several kinds of input signals cooperatively determine the weight of information transfer and storage. This study describes a peptide-based platform of materials and devices that can control the coupled conduction of protons and electrons and thus create distinct regions of synapse-like performance depending on the proton activity. We utilized tyrosine-rich peptide-based films and generalized our principles by demonstrating both memristor and synaptic devices. Interestingly, even memristive behavior can be controlled by both voltage and humidity inputs, learning and forgetting process in the device can be initiated and terminated by protons alone in peptide films. We believe that this work can help to understand the mechanism of biological memory and lay a foundation to realize a brain-like device based on ions and electrons. The structural programmability and functionality of peptide materials can be leverage for various next-generation devices such as non-volatile memories. The authors report a proton-coupled mechanism in tyrosine-rich peptides for realizing multimodal memory devices.

This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in situ sampling, and HF radar. The aim is to deepen our understanding of surface currents’ impact on contaminant dispersion, thereby improving predictive models for managing environmental incidents such as pollutant releases. Rhodamine WT dye, chosen for its significant fluorescent properties and detectability, along with drifter data, allowed us to investigate the dynamics of near-surface physical phenomena such as the Ekman current, Stokes drift, and wind-driven currents. Our research emphasizes the importance of integrating scalar tracers and Lagrangian markers in experimental designs, revealing differential dispersion behaviors due to near-surface vertical shear caused by the Ekman current and Stokes drift. During slow-current conditions, the elongation direction of the dye patch aligned well with the direction of a depth-averaged Ekman spiral, or Ekman transport. Analytical calculations of vertical shear, based on the Ekman current and Stokes drift, closely matched those derived from tracer observations. Over a 7 h experiment, the vertical diffusivity near the surface was first observed at the early stages of scalar mixing, with a value of 1.9×10−4 m2/s, and the horizontal eddy diffusivity of the dye patch and drifters reached the order of 1 m2/s at a 1000 m length scale. Particle tracking models demonstrate that while HF radar currents can effectively predict the trajectories of tracers near the surface, incorporating near-surface currents, including the Ekman current, Stokes drift, and windage, is essential for a more accurate prediction of the fate of surface floats.

The primary challenge facing silicon-based electronics, crucial for modern technological progress, is difficulty in dimensional scaling. This stems from a severe deterioration of transistor performance due to carrier scattering when silicon thickness is reduced below a few nanometres. Atomically thin two-dimensional (2D) semiconductors still maintain their electrical characteristics even at sub-nanometre scales and offer the potential for monolithic three-dimensional (3D) integration. Here we explore a strategic shift aimed at addressing the scaling bottleneck of silicon by adopting 2D semiconductors as new channel materials. Examining both academic and industrial viewpoints, we delve into the latest trends in channel materials, the integration of metal contacts and gate dielectrics, and offer insights into the emerging landscape of industrializing 2D semiconductor-based transistors for monolithic 3D integration. This Review explores adopting 2D semiconductors to overcome the scaling bottleneck of Si-based electronics. Recent trends and potential approaches for the development of 2D materials as a channel are discussed. Following this, the prerequisites, obstacles and feasible technologies for integrating contacts and gate dielectrics with 2D semiconductor-based channels are examined. The Review also provides an industrial perspective towards facilitating monolithic 3D integration.

As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm . The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days). The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.

Shape memory alloys (SMAs) demonstrate a shape memory effect and superelasticity that can provide recovery performance to structural members. In this study, a round SMA bar was designed to replace the conventional deformed steel bar, particularly within the plastic hinge section of structural members. To integrate the SMA bar and the existing steel bar, a mechanical coupler was proposed by utilizing the advantages of both one-touch and threaded couplers. Uniaxial tensile tests were conducted to analyze the performance of the proposed coupler and the mechanical properties of the SMA–steel connected bar. Stress and strain relationships were examined for steel bars mechanically connected with the SMA bar and for SMA bars before and after exhibiting the shape memory effect. To induce the shape memory effect, SMA should be heated above the finished austenite temperature. Due to the difficulty of accurately measuring strain on the heated bar using traditional contact methods, we employed digital image correlation technology for precise strain measurement of the heated SMA bar. The experimental results indicate the effective application of SMA bars within the plastic hinge region of structural members using the proposed mechanical coupler.

The purpose of this work is to quantify the effects of dissolved zinc cations on corrosion and release rates from a pre-filmed Alloy 690 steam generator tubing material that was subsequently exposed to water containing zinc. The corrosion tests were performed in circulating 2 ppm Li and 1000 ppm B water without and with 60 ppb zinc at 330 °C. Gravimetric analyses and oxide characterization revealed that the corrosion rates, release rates, and oxide thicknesses decreased by subsequent exposure of the pre-filmed Alloy 690 to zinc. These benefits are attributed to the formation of a chromium-rich inner oxide layer incorporating zinc.

Micro-LEDs (µLEDs) have been explored for augmented and virtual reality display applications that require extremely high pixels per inch and luminance 1 , 2 . However, conventional manufacturing processes based on the lateral assembly of red, green and blue (RGB) µLEDs have limitations in enhancing pixel density 3 – 6 . Recent demonstrations of vertical µLED displays have attempted to address this issue by stacking freestanding RGB LED membranes and fabricating top-down 7 – 14 , but minimization of the lateral dimensions of stacked µLEDs has been difficult. Here we report full-colour, vertically stacked µLEDs that achieve, to our knowledge, the highest array density (5,100 pixels per inch) and the smallest size (4 µm) reported to date. This is enabled by a two-dimensional materials-based layer transfer technique 15 – 18 that allows the growth of RGB LEDs of near-submicron thickness on two-dimensional material-coated substrates via remote or van der Waals epitaxy, mechanical release and stacking of LEDs, followed by top-down fabrication. The smallest-ever stack height of around 9 µm is the key enabler for record high µLED array density. We also demonstrate vertical integration of blue µLEDs with silicon membrane transistors for active matrix operation. These results establish routes to creating full-colour µLED displays for augmented and virtual reality, while also offering a generalizable platform for broader classes of three-dimensional integrated devices. We report full-colour, vertically stacked µLEDs that achieve exceptionally high array density (5,100 pixels per inch) and small size (4 µm) via a 2D material-based layer transfer technique, allowing the creation of full-colour µLED displays for augmented and virtual reality.

The purpose of this study was to investigate the effect of two-hour exposure to a forest environment on cytokine, anti-oxidant and stress levels among university students and to compare the results to those measured in urban environments. Forty-one subjects were recruited. For our crossover design, subjects were divided into two groups based on similar demographic characteristics. Group A remained in the urban environment and was asked to perform regular breathing for 2 h. Blood samples were collected and the serum levels of cytokines including interleukin-6 (IL-6), IL-8, tumor necrosis factor-α (TNF-α), and glutathione peroxidase (GPx) were examined. Subjects were moved to a small town in a rural area for an equal amount of time to exclude carryover effects, and then remained for another 2 h in a forest environment. The second set of blood samples was collected to assess the effect of exposure to the forest environment. Using the same method, Group B was first exposed to the forest environment, followed by exposure to the urban environment. Blood samples collected after the subjects were exposed to the forest environment showed significantly lower levels of IL-8 and TNF-α compared to those in samples collected after urban environment exposure (10.76 vs. 9.21, t = 4.559, p < 0.001, and 0.97 vs. 0.87, t = 4.130, p < 0.001). The GPx concentration increased significantly after exposure to the forest environment (LnGPx = 5.09 vs. LnGPx = 5.21, t = −2.039, p < 0.05).