Suppressed Lattice Disorder for Large Emission Enhancement and Structural Robustness in Hybrid Lead Iodide Perovskite Discovered by High-Pressure Isotope Effect

The soft nature of organic–inorganic halide perovskites renders their lattice particularly tunable to external stimuli such as pressure, undoubtedly offering an effective way to modify their structure for extraordinary optoelectronic properties. Here, using the methylammonium lead iodide as a representative exploratory platform, it is observed that the pressure-driven lattice disorder can be significantly suppressed via hydrogen isotope effect, which is crucial for better optical and mechanical properties previously unattainable. By a comprehensive in situ neutron/synchrotron-based analysis and optical characterizations, a remarkable photoluminescence (PL) enhancement by threefold is convinced in deuterated CD₃ND₃PbI₃, which also shows much greater structural robustness with retainable PL after high peak-pressure compression–decompression cycle. With the first-principles calculations, an atomic level understanding of the strong correlation among the organic sublattice and lead iodide octahedral framework and structural photonics is proposed, where the less dynamic CD₃ND₃⁺ cations are vital to maintain the long-range crystalline order through steric and Coulombic interactions. These results also show that CD₃ND₃PbI₃-based solar cell has comparable photovoltaic performance as CH₃NH₃PbI₃-based device but exhibits considerably slower degradation behavior, thus representing a paradigm by suggesting isotope-functionalized perovskite materials for better materials-by-design and more stable photovoltaic application.     

认知双向中继网络在κ-μ衰落信道下的中断性能分析

针对认知双向中继网络在进行数据传输时面临的复杂无线信道场景问题,采用广义κ-μ分布构建认知双向中继网络中的视距和非视距无线传输信道,推导次网络在κ-μ衰落信道下的统一中断概率,并分析次网络在多种单一和混合衰落信道情况下的中断性能。仿真结果表明,无线信道的衰落程度对次网络的中断概率影响显著,依据衰落信道类型合理设置网络参数将有助于提升次网络中断性能。     

Predicting the mechanical properties of Cu–Al2O3 nanocomposites using machine learning and finite element simulation of indentation experiments

Micromechanics model, finite element (FE) simulation of microindentation and machine learning were deployed to predict the mechanical properties of Cu–Al₂O₃ nanocomposites. The micromechanical model was developed based on the rule of mixture and grain and grain boundary sizes evolution to predict the elastic modulus of the produced nanocomposites. Then, a FE model was developed to simulate the microindentation test. The input for the FE model was the elastic modulus that was computed using the micromechanics model and wide range of yield and tangent stresses values. Finally, the output load-displacement response from the FE model, the elastic modulus, the yield and tangent strengths used for the FE simulations, and the residual indentation depth were used to train the machine learning model (Random vector functional link network) for the prediction of the yield and tangent stresses of the produced nanocomposites. Cu–Al₂O₃ nanocomposites with different Al₂O₃ concentration were manufactured using insitu chemical method to validate the proposed model. After training the model, the microindentation experimental load-displacement curve for Cu–Al₂O₃ nanocomposites was fed to the machine learning model and the mechanical properties were obtained. The obtained mechanical properties were in very good agreement with the experimental ones achieving 0.99 coefficient of determination R2 for the yield strength.     

Effect of the thicknesses of the Al2SiO5 ion conductor on the opto-electrical properties for all-solid electrochromic devices

In this study, an all-solid-state electrochromic device (ECD) with the structure of ITO/WO₃/Al₂SiO₅/NiO_x/ITO was prepared, and the effect of the Al₂SiO₅ solid electrolyte thicknesses on the opto-electrical performance was investigated. The microstructure and surface morphology were characterized using XRD, SEM and AFM, and the surface morphology and degree of surface looseness demonstrate a significant influence on the opto-electrical properties of ECDs. The charge transfer dynamics at the solid-solid interface were characterized using EIS to obtain an ionic conductivity of 4.637 × 10^-8 S/cm. CV, CA and UV–Visible spectra were employed to record the in situ electrochemical and optical properties. The results revealed that the highest optical modulation was 44.58%, the coloring and bleaching times were 14.8 s and 3.7 s, and the highest coloring efficiency was 98.17 cm²/C, which indicates that excellent opto-electrical properties were obtained. When the thickness increases, the degree of surface dense morphology transforms, and the loose morphology is more favorable for ion conductivity, which improves the opto-electrical properties. The results in this study provide insights into the understanding of Al³⁺-based all-solid-state ECDs, which promote the exploration of new types of Al³⁺ ionic conductors for all-solid-state ECDs.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Phase composition,microstructure and thermophysical properties of the Srx(Zr0.9Y0.05Yb0.05)O1.95+x ceramics

Sr_x(Zr_0.9Y_0.05Yb_0.05)O_1.95+x (x=1.0, 0.9, 0.8, 0.7) ceramics were prepared by solid state reaction sintering. The sintered Sr_1.0(Zr_0.9Y_0.05Yb_0.05)O_2.95 is a single-phase solid solution while the sintered Sr_x(Zr_0.9Y_0.05Yb_0.05)O_1.95+x (x=0.9?0.7) are composites, and a significant grain growth inhibition is observed in the sintered Sr_x(Zr_0.9Y_0.05Yb_0.05)O_1.95+x (x=1.0, 0.9). Rare-earth elements distribution in the bulk materials indicates that Yb and Y preferentially substitute Zr-sites in SrZrO₃, and the highest solubility of RE₂O₃ in pure SrZrO₃ is ～0.8 mol%. The sintered Sr_x(Zr_0.9Y_0.05Yb_0.05)O_1.95+x have high thermal expansion coefficients up to ～11.0×10^?6 K^-1 (1200°C). Sr_0.8(Zr_0.9Y_0.05Yb_0.05)O_2.75 has the lowest thermal conductivity of 1.38 W·m^-1·K^-1 at 800°C. Sr_x(Zr_0.9Y_0.05Yb_0.05)O_1.95+x (x=1.0, 0.9, 0.8) show no phase transition from 600 to 1400°C, whereas Sr_x(Zr_0.9Y_0.05Yb_0.05)O_1.95+x (x=0.9, 0.8) have excellent high-temperature phase stability over the whole investigated temperature range. Therefore, Sr_x(Zr_0.9Y_0.05Yb_0.05)O_1.95+x (x=1.0, 0.9, 0.8) are considered as promising TBCs materials that might be operated at higher temperatures compared to YSZ.