Sodium dendrite growth is responsible for short circuiting and fire hazard of metal batteries, which limits the potential application of sodium metal anode. Sodium dendrite can be effectively suppressed by applying mechanically robust electrolyte in battery systems. Herein, a composite gel polymer electrolyte (GPE) is designed and fabricated, mainly consisting of graphene oxide (GO) and polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP). With the addition of an appropriate amount of GO content, the compressive Young's modulus of 2 wt% GO+PVDF-HFP (2-GPH) composite GPE is greatly enhanced by a factor of 10, reaching 2.5 GPa, which is crucial in the suppression of sodium dendrite growth. As a result, uniform sodium deposition and ultralong reversible sodium plating/stripping (over 400 h) at high current density (5 mA cm −2 ) are achieved. Furthermore, as evidenced by molecular dynamics simulation, the GO content facilitates the sodium ion transportation, giving a high ionic conductivity of 2.3 × 10 −3 S cm −1 . When coupled with Na 3 V 2 (PO 4 ) 3 cathode in a full sodium metal battery, a high initial capacity of 107 mA h g −1 at 1 C (1 C = 117 mA g −1 ) is recorded, with an excellent capacity retention rate of 93.5% and high coulombic efficiency of 99.8% after 1100 cycles.

We investigate the phonon spectra of two-dimensional liquid dusty plasmas on a one-dimensional periodic substrate using numerical simulations. The propagation of the waves across the potential wells of the substrate is inhibited due to the confinement of the dust particles by the substrate minima. If the substrate wells are narrow or deep, one-dimensional chains of particles are formed in each minima, and the longitudinal motion of an individual chain dominates the propagation of waves along the potential wells of the substrate. Increasing the width or decreasing the depth of the substrate minima allows the particles to buckle into a zig-zag structure, and the resulting spectra develop two branches, one for sloshing motion and one for breathing motion. The repulsion between neighboring dust particles produces backward propagation of the sloshing wave for small wave numbers.

Using numerical simulations, we examine the structure and diffusion of a two-dimensional dusty plasma (2DDP) in the presence of a one-dimensional periodic substrate (1DPS) as a function of increasing substrate strength. Both the pair correlation function perpendicular to the substrate modulation and the mean squared displacement (MSD) of dust particles are calculated. It is found that both the structure and dynamics of 2DDP exhibit strong anisotropic effects, due to the applied 1DPS. As the substrate strength increases from 0, the structure order of dusty plasma along each potential well of 1DPS increases first probably due to the competition between the inter-particle interactions and the particle-substrate interactions, and then decreases gradually, which may be due to the reduced dimensionality and the enhanced fluctuations. The obtained MSD along potential wells of 1DPS clearly shows three processes of diffusion in our studied 2DDP. Between the initial ballistic and finally diffusive motion, there is the intermediate sub-diffusion discovered here, which may result from the substrate-induced distortion of the caging dynamics.

CsPbX 3 perovskite QDs can be adjusted by controlling size, morphology and com position. In addition, they have exhibited high PLQY, narrow FWHM, strong defect resistance, long carrier lifetime, and PL tunability in the entire visible spectrum range, all of which render this category of materials broad prospects in the field of optoelectronic applications. [7][8][9][10] However, owing to its ionic nature, perovskite QDs are highly susceptible to phase transition and even decomposi tion under various stimulus, including radiation, heat, moisture, oxygen. [11] Such instability problem greatly hinders the practical applications of lead halide perovskite. Besides, high defect density is often reported for colloidal perovskite QDs. These structural and surface defects would destroy the crystalline lattice of CsPbX 3 QDs, making them more vul nerable to external stimuli, and resulting in the rapid degradation of their photo electric performance. [12,13] Moreover, because of their large surface area and rich surface defective sites, inorganic perovskite QDs prepared by colloidal approach often show poor storage stability with high susceptiblity to agglomeration, thus restricting their potential commercial application. [14,15] All-inorganic perovskite quantum dots (QDs) have emerged as a new category of low-cost semiconducting luminescent materials for optoelectronic applications. However, their poor stability has become the main challenge that impedes their potential applications. Herein, Ni-doped CsPbBr 3 QDs with lead phosphate (Pb 3 (PO 4 ) 2 ) shell through surface sacrificial coating (c-Ni-CsPbBr 3 ) have been developed based on a modified thermal injection approach. The synergistic effect of Ni doping and Pb 3 (PO 4 ) 2 coating effectively improves the stability and optical performances of CsPbBr 3 QDs, including photoluminescence (PL) intensity, the average lifetime, and PL quantum yield (QY). Thus, the c-Ni-CsPbBr 3 QDs demonstrate the full width at half maxima (FWHM) and PLQY being 18.42 nm and 90.77%, respectively. The PL intensity of c-Ni-CsPbBr 3 can be maintained at 81% of its original value after being heated at 100 °C for 1 h. No phase transformation can be observed after being stored under ambient conditions (25 °C, 60% relative humidity (RH)) for 21 days. In addition, by combining green c-Ni-CsPbBr 3 QDs, red CdSe-based QDs and the blue GaN chip, a QD enhancement film (QDEF) is fabricated to form a liquid crystal display (LCD) backlit with a wide color gamut covering cover 121% of the National Television System Committee (NTSC) standard.

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The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic burden of COPD. The objectives of this study were to use machine learning approaches to identify and predict potential high-cost patients and explore the key variables of the forecasting model, by comparing differences in the predictive performance of different variable sets. Machine learning approaches were used to estimate the medical costs of COPD patients using the Medical Insurance Data of a large city in western China. The prediction models used were logistic regression, random forest (RF), and extreme gradient boosting (XGBoost). All three models had good predictive performance. The XGBoost model outperformed the others. The areas under the ROC curve for Logistic Regression, RF and XGBoost were 0.787, 0.792 and 0.801. The precision and accuracy metrics indicated that the methods achieved correct and reliable results. The results of this study can be used by healthcare data analysts, policy makers, insurers, and healthcare planners to improve the delivery of health services.

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