Search

The highly crystallized Ge films have been fabricated by magnetron sputtering and thermal annealing on silicon substrate with a graphite buffer layer which can reduce the lattice mismatch and thermal mismatch. In this paper, we demonstrate the effects of substrate temperature and annealing parameters on Ge film quality through XRD and Raman measurements. The mechanism of the Ge films crystallization during the annealing process is analyzed using the lowest energy principle and the law of Bravais.

Al2O3 dielectrics were fabricated on highly ordered pyrolytic graphite (HOPG) by atomic layer deposition (ALD) and the effects of growth temperatures and number of ALD cycles on growth behaviors were studied. It is found that Al2O3 preferentially grows along the step edges which promote the formation of Al2O3 nanowires at the initial stage. Al2O3 nanowires can exist after 100 ALD cycles at 50, 150, and 200 °C, but discontinuous Al2O3 thin films rather than nanowires are evidenced at 100 °C. Moreover, the Al2O3 layers evolve into continuous thin films with increasing number of ALD cycles. It suggests the growth behavior undergoes a transition from three-dimensional mode to quasi two-dimensional mode with increasing number of ALD cycles. The rates of transition and lateral growth are dependent on growth temperatures. Raman spectra indicate that HOPG maintains undamaged and greatly reserves its original properties after the deposition of Al2O3. The results are of great significance to the fabrication of high-quality dielectric layers on graphene as well as the related devices.

Yttria stabilized zirconia (YSZ) nanocrystals were prepared by hydrothermal synthesis and were calcinated at different temperatures and with different isothermal hold time. X-ray diffraction via whole powder pattern modeling approach was used to study the grain growth as well as the evolution of grain size distribution of YSZ nanocrystals. Results show that YSZ nanocrystals start to grow along with the grain size distribution broadening at about 300 °C. The grain growth rate and the grain size distribution of YSZ nanocrystals are dependent on the calcination temperature and the isothermal hold time. The grain growth exponent and the active energy of grain growth were calculated. Grain rotation induced grain coalescence is suggested as the predominant way of grain growth of YSZ nanocrystals.

With the development of new energy and the increase in demand, photovoltaic power generation has received more and more attention. However, due to the susceptibility of photovoltaic power generation to the influence of weather factors, especially the influence of solar irradiation, it is intermittent and fluctuating. Understanding the power generation data of a PV plant has a great effect on the subsequent photovoltaic power generation planning and photovoltaic power plant situation assessment. However, these data are often incompletely recorded due to frequent communication problems or equipment failures. Most current studies focus on photovoltaic power generation predictions, it is still a challenge to speculate the missing data around photovoltaic power plants. In this paper, a context-sensitive tensor decomposition model is proposed to intelligently fill the missing power generation entries. The experimental results show that our model can effectively fill missing data and obtain good results.

Bismuth telluride alloy thin films were prepared on quartz substrates by co-sputtering method and thermally annealed at 423∼623 K, for 1 h. It is found that the Te/Bi ratio decreases upon thermal annealing, indicating the loss of Te as a result of evaporation. This leads to the transformation of Bi 2 Te 3 thin films from n-type to p-type, and consequently the change of Seebeck coefficient from the negative value to the positive one. In addition, the grain growth occurs during thermal annealing, in particular, at higher temperature, as a result, both the electrical conductivity and the seebeck coefficient are increased.

Semiconductor nanowires are attracting intense interest as a promising substrate material for surface enhanced Raman spectroscopy (SERS). Based on vapor-liquid-solid (VLS) procedure using Au as a catalyst, silicon nanowires (SiNWs) with controllable diameters were developed by the plasma enhanced chemical vapor deposition (PECVD) system. Inspired by the fact that the morphology of SiNWs can be effectively modulated by tuning the size of catalyst droplets, we designed a needle-like SiNWs owing to the shrink and consumption of Si-Au catalyst droplets. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) analyses reveal that crystalline Si phase is possessed of amorphous SiO2 in the as-prepared coaxial nanowires. The morphology of the prepared SiNWs was examined by scanning electron microscope (SEM). As SERS active substrates, Ag nanoparticles were synthesized on surface of SiNWs by galvanic displacement. Results show that enhancement factor (EF) of SERS achieved on the prepared needle-like SiNWs/Ag is more than 10 times than that of cylindrical-shaped SiNWs/Ag for R6G molecules detection. It is supposed that needle-like SiNWs composited with Ag nanoparticles can be exploited as a new type nanosensor, environmental monitoring or biomedical diagnostics. In addition, many other needle-like semiconductor nanowires such as germanium needle-like nanowires also can be prepared by similar method in the present study.

The existing operation and maintenance system of photovoltaic power station can analyze partial equipment problems, but due to the data storage, processing and transmission capacity can hardly meet the requirements of a large number of operation data acquisitions and processing of photovoltaic power station, the operation status of photovoltaic power station cannot be comprehensively evaluated. This paper proposes to analyze the overall efficiency and key link efficiency of the photovoltaic system through long-term monitoring, extract the efficiency model parameters of the key components and important links of the photovoltaic power station, and establish the performance evaluation implementation process. At the same time, based on different actual detection conditions, photovoltaic fault diagnosis is carried out by time domain reflectometry, prediction theory comparison and sensor optimization.

With the reduction of non-renewable resources, the energy issues become more and more important and urgent. Distributed photovoltaic power stations not only provides more energy with flexible deployment, but also proposes a possibility of how to fully use the solar energy with a better efficiency. Although a distributed photovoltaic power station has many advantages and great potential, it is not easy to be operated and maintained. In recent years, the applications of machine learning in multiple fields make it possible to do the fault diagnosis of photovoltaic systems more efficiently. This paper designs a full processing system to realize the function of real-time fault diagnosis specially for distributed photovoltaic power stations, which includes the data processing, an online system to give the result of real-time diagnosis, an offline system to retrain models, and a fusion model of several machine learning algorithms. The data processing and performances of different models in this problem are introduced in detail. The fusion model that proposed in this paper acts much better than single models. Hence, the fault diagnosis system could be used in the operation and maintenance of distributed power stations.