Synthesis and evaluation of several high absorbance black pigments for spacecraft thermal control coatings

Using black coatings and materials with high light absorbance that are capable of absorbing photons at visible and longer wavelengths is a very effective way to reduce unwanted stray light, also known as optical noise, within optical equipment. These lights can be greatly reduced to a reasonable level by functional and performable black coatings that are modified to absorb incident light as much as possible by their specific pigments. In the present work, several carbonaceous pigments were synthesized for the first time from wasteful materials and their optical properties in the visible and near‐infrared ranges studied. First, MCM‐48 and SBA‐15 were synthesized at different conditions and were then used as templates for carbonaceous products. SSS‐1 (the carbonic pigment synthesized by the mixture of sucrose and sodium silicate), SSS‐2 (the carbonic pigment synthesized by the mixture of sawdust and sodium silicate), and mesoporous carbon pigments (CMK‐3 and CMK‐1 with different levels of saturations) were synthesized. Finally, their structure, morphology, and optical properties were investigated by X‐ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FE‐SEM), and Diffuse Reflectance Spectroscopy (DRS). The results indicated that the SSS‐1 pigment had a lower reflectance (below 1%) than carbon black (about 2.5%) in the visible region despite it being more cost‐effective than carbon black. The mesoporous pigments showed very high light absorbance in the visible region (about 2.5%). Compared with other black pigments, the CMK‐1 was the blackest synthesized material with a very low reflectance (about 0.05% in visible region), making it an ideal candidate as a super black pigment for reducing unwanted stray light within optical equipment.     

Dearomatization of Diesel by Solvent Extraction: Influence of the Solvent Ratio and Temperature on Diesel Raffinate Properties

To comply with the stringent environmental regulations concerning the quality of fuels the production of ultra low sulfur fuels is obligatory. Consequently, the removal of aromatics from fuels has turned to be a serious issue. This is due to the fact that the presence of aromatics in fuel deters the ultra-low sulfur fuel production. Therefore the researcher’s interest has involved the dearomatization of fuels. As a result of the dearomatization, the quality of fuels improves tremendously. Here, solvent extraction was performed to dearomatize a feedstock sample with 20.1% aromatic and 166 ppm sulfur using acetonitrile. The extraction was performed at low temperature and ambient atmospheric pressure. The aromatic contents were determined via HPLC, while the ASTM methods were employed in other parameters determination. The results showed 72% minimum yield, 8.6% aromatic content, 58–64 cetane index, 73.2 ppm sulfur content, 5.4 viscosity, RI 1.4535, aniline point 82.15, specific gravity 0.824–0.812 with API 40.32–42.88 and flash point 70–78°C. The boiling range of the produced diesel fraction raffinate (172–373°C) that corresponds to C₈–C₂₄ cuts render it a potential candidate for other petrochemical applications.     

Development of a virtual linearizer for correcting transducer static nonlinearity

This paper reports the development of an artificial neural network based virtual linearizer for correcting nonlinearity associated with transducers connected to the data-acquisition system of a computer-based measurement system. In analog processing techniques, nonlinearity is considered to be a very serious problem that at one time was solved frequently by the piecewise linear segment approach modeled by linear electronic circuits. Since the cost of microcomputers has been reduced drastically, they are currently used in most applications of measurement, including data-acquisition subsystems. Therefore, the hardware-based analog techniques of linearization are often replaced by the software-based numerical ones. In this context, it has been found that a multilayer feed-forward back-propagation network trained with the Levenberg-Marquardt learning rule provides an optimal solution to implement an efficient soft compensator to correct transducer static-nonlinearity.     

Quality Evaluation of Groundwater Resources using Geostatistical Methods (Case Study: Central Lorestan Plain,Iran)

In this research, based on the qualitative data of 40 wells, variations of water quality parameters of the Central Plain Aquifer were evaluated using kriging and IDW (Inverse Distance Weighting) methods. Owing to the normal distribution of the studied parameters (except Na⁺, SO₄^2?, and TH: total hardness), ordinary kriging was used for modeling. The analysis of the data trends indicated that all the variables were influenced by in two general trends, i.e., NW–SE and NE–SW. In fact, these trends were a result of the effect of the structural conditions on aquifer properties such as transmissivity and flow direction. Variogram analysis (based on C₀ near zero and C₀/σ² ratio between 0.0–0.5) showed that the Na⁺, TDS (total dissolved solids), Ca²⁺, and TH variables have a good spatial structure and the BOD (biochemical oxygen demand), COD (chemical oxygen demand), NO₃^?, and EC variables have poor spatial structure. The BOD, COD, NO₃^?, and EC (electrical conductivity) variables have the smallest range and isotropic distribution. On the other hand, the Ca²⁺, Mg²⁺, Na⁺, SO₄^2?, Cl^?, HCO₃^?, pH, TDS, TH and Alk (alkalinity) parameters are characterized by anisotropic distributions. The Na⁺, TDS, Ca²⁺, and TH variables have the largest range. The results showed that both the IDW and kriging methods have close estimates to one another. The pH variable decreases toward the outlet whereas the EC and TDS variables increase along the direction of water flow and toward the outlet. The distributions of the BOD and COD variables do not perfectly match with the aggregation of industrial activities in the central part as well as the agricultural activities in the southeastern and central parts of the aquifer. The distributions of the Ca²⁺, Mg²⁺, and Alk variables completely follow the geology condition and regional spread of carbonate formations. The Na⁺ concentration increases from the center toward the outlet. The concentration of the Cl^? variable is the highest in the central part of the plain due to the concentration of agricultural and industrial activities. The distribution of the SO₄^2? variable is influenced by a natural factor (lithology), especially in the southeastern parts and the outlet as well as artificial factors (agricultural and industrial activities) in the central and southeastern parts of the aquifer. The NO₃^? variable, which is directly influenced by agricultural and livestock-farming activities, has its maximum concentration in the southeastern areas.     

Copper effects in mechanical,thermal and electrical properties of rapidly solidified eutectic Sn–Ag alloy

The Sn–3.5 wt%Ag alloy considered as a good alternative to Pb–Sn alloys. This study aims to investigate the effects of Cu or Sb additions by 3 or 5 wt% to melt-spun Sn–3.5%Ag alloy. Ternary melt-spun Sn–Ag–Cu and Sn–Ag–Sb alloys investigated using X-ray diffractions (XRD), Scanning electron microscope (SEM), Dynamic resonance technique (DRT), Instron machine, Vickers hardness tester and Differential scanning calorimetry (DSC). The results revealed that the microstructures of the β-Sn phase, Ag₃Sn and Cu₃Sn intermetallic compounds (IMCs) in the solder matrices were refined due to the effect of Cu additions and melt-spun process. Moreover, increasing Cu content promotes Ag₃Sn intermetallic compound (IMC) formation. Consequently, the addition of “3 wt%” of Cu reduced the creep rate ? from (3.79?×?10^?3) to (1.65?×?10^?3) and delayed the fracture point. The tensile results showed an improvement in Young’s modulus by 47% (30.3 GPa), ultimate tensile strength (UST) by 11.6% (23.9 MPa), and in toughness by 20.5% (952.32 J/m³) compared to the eutectic Sn–Ag alloy. Vickers hardness has improved by 3.3% (136.71 MPa) and thermal activation energy by 54% (90.40 KJ/mol) when compared with that of eutectic Sn–Ag alloy. Those improvements are related to the lack of lattice strain from 7.56?×?10^?4 without “3 wt%” of Cu to 5.26?×?10^?4 with “3 wt%” of Cu. Its melting temperature (T_m) increased by 3 °C due to Ag₃Sn IMC increased and Cu₃Sn formation, but the pasty rang (mushy zone) decreased by 4 °C with “3 wt%” of Cu. The small lattice strains resulted with “3 wt%” of Cu made the electrical resistivity of this alloy more stable at elevated temperatures. The mechanical, thermal and electrical improvements of Sn_93.5–Ag_3.5–Cu₃ alloy provide good physical performance for soldering process and electronic assembly.     

Photo-electrocatalytic methanol oxidation on Pd decorated BiOI nanoparticles in alkali

Journal of Materials Science - Palladium–bismuth oxoiodide nanocomposites of various molar ratios of Pd and BiOI have been synthesized by the facile solvothermal method following the chemical...     

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.     

An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

This research proposes an improved hybrid fusion scheme for non-subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub-bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub-bands. The entropy of square of the coefficients and weighted sum-modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.     

Core techniques of question answering systems over knowledge bases: a survey

The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data. Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.     

Metallic Nanowire Coupled CsPbBr3 Quantum Dots Plasmonic Nanolaser

Plasmonic nanolasers provide a valuable opportunity for expanding sub-wavelength applications. Due to the potential of on-chip integration, semiconductor nanowire (NW)-based plasmonic nanolasers that support the waveguide mode attract a high level of interest. To date, perovskite quantum dots (QDs) based plasmonic lasers, especially nanolasers that support plasmonic-waveguide mode, are still a challenge and remain unexplored. Here, metallic NW coupled CsPbBr₃ QDs plasmonic-waveguide lasers are reported. By embedding Ag NWs in QDs film, an evolution from amplified spontaneous emission with a full width at half maximum (FWHM) of 6.6 nm to localized surface plasmon resonance (LSPR) supported random lasing is observed. When the pump light is focused on a single Ag NW, a QD-NW coupled plasmonic-waveguide laser with a much narrower emission peak (FWHM = 0.4 nm) is realized on a single Ag NW with the uniform polyvinylpyrrolidone layer. The QDs serve as the gain medium while the Ag NW serves as a resonant cavity and propagating plasmonic lasing modes. Furthermore, by pumping two Ag NWs with different directions, a dual-wavelength lasing switch is realized. The demonstration of metallic NW coupled QDs plasmonic nanolaser would provide an alternative approach for ultrasmall light sources as well as fundamental studies of light matter interactions.