Tracking the changes in the structural, optical and photoluminescent properties of CuCo2O4/MnS nanocomposites with different composition ratios

(1−x)CuCo2O4/xMnS (x = 0, 0.25, 0.5) nanocomposite samples were formed using hydrothermal and thermolysis procedures. X-ray diffraction (XRD) phase analysis showed the formation of only CuCo2O4 phase necessitating the inclusion of Mn and S ions into the CuCo2O4 lattice. Fourier-transform infrared spectroscopy (FTIR) analyses confirmed the presence of Mn and S ions in the nanocomposite samples. Rietveld refinement method was applied to determine the cation distribution of the different ions between different sites. The cell parameter (a) has no fixed trend of change. The average crystallite size is almost the same for all samples with an average of 15 nm. The effect of insertion of Mn and S ions into the CuCo2O4 on the diffused absorbance, extinction coefficient, refractive index, dielectric properties, and nonlinear optical parameters was discussed in detail. The pristine CuCo2O4 nanoparticles have two direct optical band gaps (1.65, 2.74) eV which are decreased to (1.59, 2.56) and (1.58, 2.54) eV for the MnS content x = 0.25 and 0.5, respectively. The two indirect optical band gaps of pristine CuCo2O4 changed irregularly as the MnS amount increased in the nanocomposite. The PL spectrum of CuCo2O4 is shifted to higher wavelength in the visible region upon alloying with MnS. The photoluminescence (PL) intensity of the nanocomposite samples is smaller than that of CuCo2O4 sample. The emitted PL colors depended on the amount of Mn and S ions in the CuCo2O4 matrix.

The supports’ tendency to generate oxygen vacancies and the Ni/ZrO2 and Ni/Mg(Al)O catalysts performance in the CO2 methanation

The Ni/ZrO2, Ni/Mg(Al)O, and Ni/SiO2 catalysts were employed in the CO2 methanation. The catalysts were characterized by XPS, XRF, XRD (Rietveld refinement method), TPR, EPR, BET, CO2+H2-TPSR, CO+H2-TPSR, CO2-TPD, CO-TPD,...

Role of Ti3AlC2 MAX phase on characteristics of in-situ synthesized TiAl intermetallics. Part II: Phase evolution

In this research, the 2nd part of a series of papers on the processing and characterization of TiAl–Ti3AlC2 composites, the phase evolution during the manufacturing process was investigated by X-ray diffraction (XRD) analysis and Rietveld refinement method. Metallic Ti and Al powders with different amounts of previously-synthesized Ti3AlC2 additives (10, 15, 20, 25 and 30 wt%) were ball-milled and densified by spark plasma sintering (SPS) under 40 MPa for 7 min at 900 °C. Before the sintering process, XRD test verified that the powder mixtures contained metallic Ti and Al as well as Ti3AlC2 and TiC (lateral phase synthesized with Ti3AlC2) phases. In the sintered composites, the in-situ synthesis of TiAl and Ti3Al intermetallics as well as the presence of Ti3AlC2 and the formation and Ti2AlC MAX phases were disclosed. The weight percentage of each phase in the final composition of the samples and the crystallite size of different phases were calculated by the Rietveld refinement method based on the XRD patterns. The size of Ti3AlC2 crystallites in sintered samples was compared with the crystallite size of synthesized Ti3AlC2 powder.

Understanding the Seebeck coefficient of LaNiO3 compound in the temperature range 300-620 K

Transition metal oxides have been attracted much attention in thermoelectric community from the last few decades. In the present work, we have synthesized LaNiO3 by a simple solution combustion process. To analyze the crystal structure and structural parameters we have used Rietveld refinement method wherein FullProf software is employed. The room temperature x-ray diffraction indicates the rhombohedral structure with space group R 3 c (No. 167). The refined values of lattice parameters are a = b = c = 5.4071 Å. Temperature dependent Seebeck coefficient (S) of this compound has been investigated by using experimental and computational tools. The measurement of S is conducted in the temperature range 300-620 K. The measured values of S in the entire temperature range have negative sign that indicates n-type character of the compound. The value of S is found to be ∼ -8 µV/K at 300 K and at 620 K this value is ∼ -12 µV/K. The electronic structure calculation is carried out using DFT+U method due to having strong correlation in LaNiO3. The calculation predicts the metallic ground state of the compound. Temperature dependent S is calculated using BoltzTraP package and compared with experiment. The best matching between experimental and calculated values of S is observed when self-interaction correction is employed as double counting correction in spin-polarized DFT + U (= 1 eV) calculation. Based on the computational results maximum power factors are also calculated for p-type and n-type doping of this compound.

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

Technological Aspects of Producing Surface Composites by Friction Stir Processing—A Review

FSP (friction stir processing) technology is a modern grain refinement method that is setting new trends in surface engineering. This technology is used not only to modify the microstructure of the surface layer of engineering materials, but increasingly more often also to produce surface composites. The application potential of FSP technology lies in its simplicity and speed of processing and in the wide range of materials that can be used as reinforcement in the composite. There are a number of solutions enabling the effective and controlled introduction of the reinforcing phase into the plasticized matrix and the production of the composite microstructure in it. The most important of them are the groove and hole methods, as well as direct friction stir processing. This review article discusses the main and less frequently used methods of producing surface composites using friction stir processing, indicates the main advantages, disadvantages and application limitations of the individual solutions, in addition to potential difficulties in effective processing. This information can be helpful in choosing a solution for a specific application.

Fifty years of Rietveld refinement: Methodology and guidelines in superconductors and functional magnetic nanoadsorbents

The Rietveld refinement method has taken high relevance since its creation. Nowadays, it is an useful tool in many fields of industry, materials science, and technology. For these reasons, it becomes a need for scientists and engineers whom pretend to use it for proper analysis of their materials. However, the initiation in the method can be slow, taking into account the accelerated rhythm of the research and economic demands. Thus, this work is an intend to fulfill this hole, providing the basic foundations and methodology of the Rietveld refinement in a brief way, this along with the results of its application in superconducting samples of Yttrium Barium Copper Oxide and magnesium diboride, and functional magnetic nanoadsorbents of maghemite and a multiphasic composite (iron oxide, hydroxyapatite and secondary phases). In the process, an in-detail protocol was designed and provided. It was concluded that the samples were successfully refined and that this work represents a fast introduction to the Rietveld method for which beginners can obtain good results while making correct interpretations ofthe whole refinement process.

An iterative refinement method of point cloud for binocular vision 3D reconstruction

This paper introduces a new binocular stereo deep learning network based on point cloud, which can realize higher precision point cloud reconstruction through continuous iteration of the network. Our method directly carries out point cloud processing on the target, calculates the difference between the current depth map and the real depth, estimates the loss according to the predicted point cloud and the information of the dual view input image, and then uses the appropriate loss function to iteratively process the point cloud. In addition, we can customize the number of iterations to achieve higher precision point cloud effect. The proposed network basically achieves good results on KITTI data set.

Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers

Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data and the bin-based calculation. In this paper, we propose to use the deep segmentation model UNet to learn base-wise RD patterns surrounding breakpoints of known SVs. We integrate model predictions with an RD-based SV caller to enhance breakpoints in single-nucleotide resolution. We show that UNet can be trained with a small amount of data and can be applied both in-sample and cross-sample. An enhancement pipeline named RDBKE significantly increases the number of SVs with more precise breakpoints on simulated and real data. The source code of RDBKE is freely available at https://github.com/yaozhong/deepIntraSV.