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.     

Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.     

Designing amorphous formulations of polyphenols with naringin by spray-drying for enhanced solubility and permeability

Naringin (NAR), a major flavanone (FVA) glycoside, is a component of food mainly obtained from grapefruit. We used NAR as a food additive to improve the solubility and permeability of hydrophobic polyphenols used as supplements in the food industry. The spray-dried particles (SDPs) of NAR alone show an amorphous state with a glass transition temperature (T_g) at 93.2 °C. SDPs of hydrophobic polyphenols, such as flavone (FVO), quercetin (QCT), naringenin (NRG), and resveratrol (RVT) were prepared by adding varying amounts of NAR. All SDPs of hydrophobic polyphenols with added NAR were in an amorphous state with a single T_g, but SDPs of hydrophobic polyphenols without added NAR showed diffraction peaks derived from each crystal. The SDPs with NAR could keep an amorphous state after storage at a high humidity condition for one month, except for SDPs of RVT/NAR. SDPs with NAR enhanced the solubility of hydrophobic polyphenols, especially NRG solubility, which was enhanced more than 9 times compared to NRG crystal. The enhanced solubility resulted in the increased membrane permeability of NRG. The antioxidant effect of the hydrophobic NRG was also enhanced by the synergetic effect of NAR. The findings demonstrated that NAR could be used as a food additive to enhance the solubility and membrane permeability of hydrophobic polyphenols.     

Exploring the feasibility of green hydrogen production using excess energy from a country-scale 100% solar-wind renewable energy system

When planning large-scale 100% renewable energy systems (RES) for the year 2050, the system capacity is usually oversized for better supply-demand matching of electrical energy since solar and wind resources are highly intermittent. This causes excessive excess energy that is typically dissipated, curtailed, or sold directly. The public literature shows a lack of studies on the feasibility of using this excess for country-scale co-generation. This study presents the first investigation of utilizing this excess to generate green hydrogen gas. The concept is demonstrated for Jordan using three solar photovoltaic (PV), wind, and hybrid PV-wind RESs, all equipped with Lithium-Ion battery energy storage systems (ESSs), for hydrogen production using a polymer electrolyte membrane (PEM) system. The results show that the PV-based system has the highest demand-supply fraction (>99%). However, the wind-based system is more favorable economically, with installed RES, ESS, and PEM capacities of only 23.88 GW, 2542 GWh, and 20.66 GW. It also shows the highest hydrogen annual production rate (172.1 × 10³ tons) and the lowest hydrogen cost (1.082 USD/kg). The three systems were a better option than selling excess energy directly, where they ensure annual incomes up to 2.68 billion USD while having payback periods of as low as 1.78 years. Furthermore, the hydrogen cost does not exceed 2.03 USD/kg, which is significantly lower than the expected cost of hydrogen (3 USD/kg) produced using energy from fossil fuel-based systems in 2050.