VO2/GaAs异质结的制备及其光电特性研究

采用直流磁控溅射和后退火氧化工艺在p型GaAs单晶衬底上成功制备了n-VO_2/pGaAs异质结,研究了不同退火温度和退火时间对VO_2/GaAs异质结性能的影响,并分析其结晶取向、化学组分、膜层质量以及光电特性。结果表明,在退火时间2 h和退火温度693 K下能得到相变性能最佳的VO_2薄膜,相变前后电阻变化约2个数量级。VO_2/GaAs异质结在308 K、318 K和328 K温度下具有较好的整流特性,对应温度下的阈值跳变电压分别为6.9 V、6.6 V和6.2 V,该结果为基于VO_2相变特性的异质结光电器件的设计与应用提供了可行性。     

基于VMD和BSA-KELM的高陡边坡位移预测模型研究

边坡位移的时间序列曲线存在复杂的非线性特性,传统的预测模型精度不足以满足预测要求。为此提出了基于变分模态分解的鸟群优化-核极限学习机的预测模型,并用于河北省某水泥厂的边坡位移预测。该方法首先采用VMD把边坡位移序列分解为一系列的有限带宽的子序列,再对各子序列分别采用相空间重构并用核极限学习机预测,采用鸟群算法优化相空间重构的嵌入维度和KELM中惩罚系数和核参数三个数值,以取得最优预测模型。最后将各个子序列预测值叠加,得到边坡位移的最终预测值。结果表明：和KELM、BSA-KELM、EEMD-BSA-KELM模型相比,基于VMD的BSA-KELM预测精度更高,为边坡位移的预测提供一种有效的方法。     

Magnetic properties,phase evolution,hollow structure and biomedical application of hematite (α-Fe2O3) and QUAIPH

We investigate synthesis, phase evolution, hollow and porous structure and magnetic properties of quasi-amorphous intermediate phase (QUAIPH) and hematite (α-Fe₂O₃) nanostructure synthesized by annealing of akaganeite (β-FeOOH) nanorods. It is found that the annealing temperature determines the phase composition of the products, the crystal structure/size dictates the magnetic properties whereas the final nanorod morphology is determined by the starting material. Annealing of β-FeOOH at ～300 °C resulted in the formation of hollow QUAIPH nanorods. The synthesized material shows low-cytotoxicity, superparamagnetism and good transverse relaxivity, which is rarely reported for QUAIPH. The QUAIPH nanorods started to transform to porous hematite nanostructures at ～350 °C and phase transformation was completed at 600 °C. During the annealing, the crystal structure changed from monoclinic (akaganeite) to quasi-amorphous and rhombohedral (hematite). Unusually, the crystallite size first decreased (akaganeite → QUAIPH) and then increased (QUAIPH → hematite) during annealing whereas the nanorods retained particle shape. The magnetic properties of the samples changed from antiferromagnetic (akaganeite) to superparamagnetic with blocking temperature T_B = 84 K (QUAIPH) and finally to weak-ferromagnetic with the Morin transition at T_M = 244 K and high coercivity H_C = 1652 Oe (hematite). The low-cytotoxicity and MRI relaxivity (r₂ = 5.80 mM^?1 s^?1 (akaganeite), r₂ = 4.31 mM^?1 s^?1 (QUAIPH) and r₂ = 5.17 mM^?1 s^?1 (hematite)) reveal potential for biomedical applications.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Nonlinearity in regulating the metal to insulator transition of ReNiO3 towards low temperature range

Among the existing material family of the correlated oxides, the rare earth nickelates (ReNiO₃) exhibit broadly adjustable metal to insulator transition (MIT) properties that enables correlated electronic applications, such as thermistors, thermochromics, and logical devices. Nevertheless, how to accurately control the critical temperature (T_MIT) of ReNiO₃ via the co-occupation of the rare-earth elements is yet worthy to be further explored. Herein, we demonstrate the non-linearity in adjusting the T_MIT of ReNiO₃ towards lower temperatures via introducing Pr co-occupation within ReNiO₃ (e.g., Pr_xNd_1-xNiO₃ and Pr_xSm_1-xNiO₃) as synthesized by KCl molten-salt assisted high oxygen pressure reaction approach. Although the T_MIT is effectively reduced via Pr substitution, it does not strictly follow a linear relationship, in particular, when there is large difference in the ionic radius of the co-occupation rare-earth elements. Furthermore, the most significant deviation in T_MIT from the expected linear relationship appears at an equal co-occupation ratio of the two different rare-earth elements, while the abruption in the variation of resistivity across T_MIT is also reduced. The present work highlights the importance to use adjacent rare-earth elements with co-occupation ratio away from 1:1 for achieving more linear adjustment in designing the metal to insulator transition properties for ReNiO₃.     

Precipitous weakening of quartz at the α–β phase inversion

Crystalline quartz has long been identified as among the weakest of abundant crustal minerals. This weakness is particularly evident around the α–β phase inversion at 573°C, in which Si–O bonds undergo a displacive structural transformation from trigonal to hexagonal symmetry. Here we present data using indentation testing methodologies that highlight the precipitous extent of the transformational weakening. Although the indentations are localized over relatively small specimen contact areas, the data quantify the essential deformation and fracture properties of quartz in a predominantly (but not exclusively) compressive stress field, at temperatures and pressures pertinent to conditions in the earth's crust.     

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.     

Microstructure,wear and corrosion characteristics of multiple-reinforced (SiC–B4C–Al2O3) Al matrix composites produced by liquid metal infiltration

In this study, AA7075 aluminum matrix composites reinforced with the combination of SiC, Al₂O₃, and B₄C particles were fabricated by the liquid metal infiltration method. The effects of the relative ratio of B₄C and Al₂O₃ particles on the microstructural, wear, and corrosion features of the composite samples were analyzed using XRD, light metal microscopy, SEM, EDS, Brinell hardness, ball-on-disc type tribometer, and potentiodynamic polarization devices. It was determined that infiltration occurred more successfully, and homogenously distributed particles with reduced porosity were obtained as the amount of Al₂O₃ increased. Worn surface studies revealed that the specimens were predominantly worn by abrasion and adhesion. The increase in B₄C/Al₂O₃ ratio caused a decrease in the hardness and wear strength, whereas it increased the corrosion resistance.     

Environmental effects on the strength and impact damage resistance of alumina based oxide/oxide ceramic matrix composites

In this study the effects of high temperature and moisture on the impact damage resistance and mechanical strength of Nextel 610/alumina silicate ceramic matrix composites were experimentally evaluated. Composite laminates were exposed to either a 1050°C isothermal furnace-based environment for 30 consecutive days at 6 h a day, or 95% relative humidity environment for 13 consecutive days at 67°C. Low velocity impact, tensile and short beam strength tests were performed on both ambient and environmentally conditioned laminates and damage was characterized using a combination of non-destructive and destructive techniques. High temperature and humidity environmental exposure adversely affected the impact resistance of the composite laminates. For all the environments, planar internal damage area was greater than the back side dent area, which in turn was greater than the impactor side dent area. Evidence of environmental embrittlement through a stiffer tensile response was noted for the high temperature exposed laminates while the short beam strength tests showed greater propensity for interlaminar shear failure in the moisture exposed laminates. Destructive evaluations exposed larger, more pronounced delaminations in the environmentally conditioned laminates in comparison to the ambient ones. External damage metrics of the impactor side dent depth and area directly influenced the post-impact tensile strength of the laminates while no such trend between internal damage area and residual strength could be ascertained.     

基于改进SOA-VMD的磁声发射信号去噪算法

磁声发射（MAE）是铁磁性材料磁化过程中产生的声发射信号，在构件应力检测和微观损伤检测中有着广泛的应用。针对MAE信号非稳态、复杂性、衰减性等特点，提出海鸥算法结合变分模态分解（SOA-VMD）的去噪方法，为克服海鸥算法求解过程中易陷入局部最优解问题，利用柯西变异算子产生随机迭代过程，使改进算法即柯西变异海欧算法（CVSOA）跳出早熟收敛。采用以幅值谱熵为适应度函数，优化VMD算法中分解模态个数K和二次惩戒因子α两个参数，将含噪声的MAE信号进行VMD分解重构。经仿真信号和实际检测信号分析表明，改进后的CVSOA-VMD算法全局寻优能力和去噪性能优于传统的SOA-VMD算法，降噪后的MAE信号特征值对于不同应力下均方根、偏斜度特征值的重复性更好，可靠性更高。