Deposition,opto-electronic and structural characterization of polymorphous silicon thin films to be applied in a solar cell structure

In this study we analyze the optoelectronic properties and structural characterization of hydrogenated polymorphous silicon thin films as a function of the deposition parameters. The films were grown by plasma enhanced chemical vapor deposition (PECVD) using a gas mixture of argon (Ar), hydrogen (H₂) and dichlorosilane (SiH₂Cl₂). High-resolution transmission electron microscopy images and Raman measurements confirmed the existence of very different internal structures (crystalline fractions from 12% to 54%) depending on the growth parameters. Variations of as much as one order of magnitude were observed in both the photoconductivity and effective absorption coefficient between the samples deposited with different dichlorosilane/hydrogen flow rate ratios. The optical and transport properties of these films depend strongly on their structural characteristics, in particular the average size and densities of silicon nanocrystals embedded in the amorphous silicon matrix. From these results we propose an intrinsic polymorphous silicon bandgap grading thin film to be applied in a p–i–n junction solar cell structure. The different parts of the solar cell structure were proposed based on the experimental optoelectronic properties of the pm-Si:H thin films studied in this work.     

燃料电池空气电极的孔道结构调控

燃料电池是将化学能转化成电能的装置，其空气电极催化层的设计，既要包含丰富的、易于接近的反应活性位，也要具备高度连通的电子、质子以及反应物、产物传质通道，因此电极必须具有特定三维几何结构形貌和有序分布的各功能化孔道，确保催化活性位得以充分利用以及反应可以连续进行。针对催化剂孔道的几何结构调控，本文调研了最近报道的一系列研究工作，从模板法、高温相变法、模板/相变复合方法以及基于金属有机框架材料进行孔道设计等四种主要方法出发，综述了该领域的最新研究进展。     

Effect of hydrometallurgical process parameters on the Mn2O3 nano catalysts derived from spent batteries used in the plasma catalytic oxidation of BTX

Manganese oxide catalysts have been synthesized from the used batteries via hydrometallurgical method and effect of hydrometallurgical parameters such as the effect of acid type (H₂SO₄, HNO₃, HCl), acid concentration (0.5, 1, 1.5, 2 %v/v) and powder to acid ratio (1/50, 1/60, 1/70, 1/80) were in detail investigated. The physico-chemical properties of as-prepared catalysts were characterized by FT-IR, XRD, FESEM, EDX, BET, TEM, and TPR-H₂ analysis. The activity of as-prepared catalysts were investigated towards the oxidation of benzene, toluene, and xylene (BTX) in a plasma-catalytic process. The results show that benzene and toluene conversion were almost constant in the range of 97–98% in case of various acid types, acid concentrations and solid to liquid ratios. However, the xylene conversion were varied in case of different hydrometallurgical factors. The highest xylene conversion was obtained in the presence of MnS0.5–60, which was prepared using H₂SO₄ with concentration of 0.5%v/v and solid to liquid ratio of 1/60. The effect of the input voltage and BTX flow rate on the BTX conversion was also investigated using MnS0.5–60 catalyst in detail.     

Precise Extraction of Charge Carrier Mobility for Organic Transistors

Unreliable mobility values, and particularly greatly overestimated values and severely distorted temperature dependences, have recently hampered the development of the organic transistor field. Given that organic field‐effect transistors (OFETs) have been routinely used to evaluate mobility, precise parameter extraction using the electrical properties of OFETs is thus of primary importance. This review examines the origins of the various mobilities that must be determined for OFET applications, the relevant extraction methods, and the data selection limitations, which help in avoiding conceptual errors during mobility extraction. For increased precision, the review also discusses device fabrication considerations, calibration of both the specific gate‐dielectric capacitance and the threshold voltage, the contact effects, and the bias and temperature dependences, which must actually be handled with great care but have mostly been overlooked to date. This review serves as a systematic overview of the OFET mobility extraction process to ensure high precision and will also aid in improving future research.     

Mutagenic Effects of Iron Oxide Nanoparticles on Biological Cells

In recent years, there has been an increased interest in the design and use of iron oxide materials with nanoscale dimensions for magnetic, catalytic, biomedical, and electronic applications. The increased manufacture and use of iron oxide nanoparticles (IONPs) in consumer products as well as industrial processes is expected to lead to the unintentional release of IONPs into the environment. The impact of IONPs on the environment and on biological species is not well understood but remains a concern due to the increased chemical reactivity of nanoparticles relative to their bulk counterparts. This review article describes the impact of IONPs on cellular genetic components. The mutagenic impact of IONPs may damage an organism’s ability to develop or reproduce. To date, there has been experimental evidence of IONPs having mutagenic interactions on human cell lines including lymphoblastoids, fibroblasts, microvascular endothelial cells, bone marrow cells, lung epithelial cells, alveolar type II like epithelial cells, bronchial fibroblasts, skin epithelial cells, hepatocytes, cerebral endothelial cells, fibrosarcoma cells, breast carcinoma cells, lung carcinoma cells, and cervix carcinoma cells. Other cell lines including the Chinese hamster ovary cells, mouse fibroblast cells, murine fibroblast cells, Mytilus galloprovincialis sperm cells, mice lung cells, murine alveolar macrophages, mice hepatic and renal tissue cells, and vero cells have also shown mutagenic effects upon exposure to IONPs. We further show the influence of IONPs on microorganisms in the presence and absence of dissolved organic carbon. The results shed light on the transformations IONPs undergo in the environment and the nature of the potential mutagenic impact on biological cells.     

Study of metal assisted anisotropic chemical etching of silicon for high aspect ratio in crystalline silicon solar cells

Textured surface is commonly used to enhance the efficiency of silicon solar cells by reducing the overall reflectance and improving the light scattering. In this study, a comparison between isotropic and anisotropic etching methods was investigated. The deep funnel shaped structures with high aspect ratio are proposed for better light trapping with low reflectance in crystalline silicon solar cells. The anisotropic metal assisted chemical etching (MACE) was used to form the funnel shaped structures with various aspect ratios. The funnel shaped structures showed an average reflectance of 14.75% while it was 15.77% for the pillar shaped structures. The average reflectance was further reduced to 9.49% using deep funnel shaped structures with an aspect ratio of 1:1.18. The deep funnel shaped structures with high aspect ratios can be employed for high performance of crystalline silicon solar cells.     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.