Structure-dependent activity and stability of Al2O3 supported Rh catalysts for the steam reforming of n-dodecane

Steam reforming of liquid hydrocarbon fuels is an appealing way for the production of hydrogen. In this work, the Rh/Al₂O₃ catalysts with nanorod (NR), nanofiber (NF) and sponge-shaped (SP) alumina supports were successfully designed for the steam reforming of n-dodecane as a surrogate compound for diesel/jet fuels. The catalysts before and after reaction were well characterized by using ICP, XRD, N₂ adsorption, TEM, HAADF-STEM, H₂-TPR, CO chemisorption, NH₃-TPD, CO₂-TPD, XPS, Al²⁷ NMR and TG. The results confirmed that the dispersion and surface structure of Rh species is quite dependent on the enclosed various morphologies. Rh/Al₂O₃-NR possesses highly dispersed, uniform and accessible Rh particles with the highest percentage of surface electron deficient Rh⁰ active species, which due to the unique properties of Al₂O₃ nanorod including high crystallinity, relatively large alumina particle size, thermal stability, and large pore volume and size. As a consequent, Rh/Al₂O₃-NR catalyst exhibited superior catalytic activity towards steam reforming reactions and hydrogen production rate over other two catalysts. Especially, Rh/Al₂O₃-NR catalyst showed the highest hydrogen production rate of 87,600 mmol g_fuel^?1 g_Rh^?1min^?1 among any Rh-based catalysts and other noble metal-based catalysts to date. After long-term reaction, a significant deactivation occurred on Rh/Al₂O₃–NF and Rh/Al₂O₃-SP catalysts, due to aggregation and sintering of Rh metal particles, coke deposition and poor hydrothermal stability of nanofibrous structure. In contrast, the Rh/Al₂O₃-NR catalyst shows excellent reforming stability with negligible coke formation. No significantly sintering and aggregation of the Rh particles is observed after long-term reaction. Such great catalyst stability can be explained by the role of hydrothermal stable nanorod alumina support, which not only provides a unique environment for the stabilization of uniform and small-size Rh particles but also affords strong surface basic sites.     

Investigation of the changes in physical properties of PES/PVC fabrics after aging

The aim of this work was to investigate the physical and mechanical performance of architectural polyester (PES)–poly(vinyl chloride) (PVC) membranes exposed to different artificial aging conditions. Two commercially available architectural membranes were chosen as research objects. The durability of the PES/PVC fabrics was evaluated by the loss in mechanical performance, scanning electron microscopy, and X-ray diffraction analysis in order to understand the effect of the degradation agents on the surface of the membranes. The mechanical performance of the PES/PVC membranes was unchanged. Scanning electron microscopy images of the tested materials showed initial cracks after aging. The X-ray fluorescence analysis showed that at the time of aging, the amount of Cl and Si decreased slightly, while Ti decreased by half, and Ca by volume increased twice. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47523.     

Blends of Bio-Based Poly(Limonene Carbonate) with Commodity Polymers

In this study, blends of the bio-based poly(limonene carbonate) (PLimC) with different commodity polymers are investigated in order to explore the potential of PLimC toward generating more sustainable polymer materials by reducing the amount of petro- or food-based polymers. PLimC is employed as minority component in the blends. Next to the morphology and thermal properties of the blends the impact of PLimC on the mechanical properties of the matrix polymers is studied. The interplay of incompatibility and zero-shear melt viscosity contrast determines the blend morphology, leading for all blends to a dispersed droplet morphology for PLimC. Blends with polymers of similar structure to PLimC (i.e., aliphatic/aromatic polyester) show the best performance with respect to mechanical properties, whereas blends with polystyrene or poly(methyl methacrylate) are too brittle and polyamide 12 blends show very low elongations at break. In blends with Ecoflex (poly(butylene adipate-co-terephthalate)) and Arnitel EM400 (copoly(ether ester)) with poly(butylene terephthalate) hard and polytetrahydrofuran soft segments) a threefold increase in E-modulus can be achieved, while keeping the elongation at break at reasonable high values of ≈200%, making these blends highly interesting for applications.     

The effect of particle size distribution on the microstructure and properties of Al2O3 ceramics formed by stereolithography

Stereolithography (SL) shows advantages for preparing alumina-based ceramics with complex structures. The effects of the particle size distribution, which strongly influence the sintering properties in ceramic SL, have not been systematically explored until now. Herein, the influence of the particle size distribution on SL-manufactured alumina ceramics was investigated, including bending strength at room temperature, post-sintering shrinkage, porosity, and microstructural morphology. Seven particle size distributions of alumina ceramics were studied (in μm/μm: 30/5, 20/3, 10/2, 5/2, 5/0.8, 3/0.5, and 2/0.3); a coarse:fine particle ratio of 6:4 was maintained. At the same sintering temperature, the degree of sintering was greater for finer particle sizes. The particle size distribution had a larger influence on flexural strength, porosity and shrinkage than sintering temperature when the particle size distribution difference reached 10-fold but was weaker for 10 μm/2 μm, 5 μm/2 μm and 5 μm/0.8 μm. The sintering shrinkage characteristics of cuboid samples with different particle sizes were studied. The use of coarse particles influenced the accuracy of small-scale samples. When the particle size was comparable to the sample width, such as 30 μm/5 μm and 5 mm, the width shrinkage was consistent with the height shrinkage. When the particle size was much smaller than the sample width, such as 2 μm/0.3 μm and 5 mm, the width shrinkage was consistent with the length shrinkage. The results of this study provide meaningful guidance for future research on applications of SL and precise control of alumina ceramics through particle gradation.     

Morphological study of thin films: Simulation and experimental insights using horizontal visibility graph

We studied the morphological nature of various thin films such as silicon carbide (SiC), diamond (C), germanium (Ge), and gallium nitride (GaN) on silicon substrate Si(100) using the pulsed laser deposition (PLD) method and Monte Carlo simulation. We, for the first time, systematically employed the visibility algorithm graph to meticulously study the morphological features of various PLD grown thin films. These thin-film morphologies are investigated using random distribution, Gaussian distribution, patterned heights, etc. The nature of the interfacial height of individual surfaces is examined by a horizontal visibility graph (HVG). It demonstrates that the continuous interfacial height of the silicon carbide, diamond, germanium, and gallium nitride films are attributed to random distribution and Gaussian distribution in thin films. However, discrete peaks are obtained in the brush and step-like morphology of germanium thin films. Further, we have experimentally verified the morphological nature of simulated silicon carbide, diamond, germanium, and gallium nitride thin films were grown on Si(100) substrate by pulsed laser deposition (PLD) at elevated temperature. Various characterization techniques have been used to study the morphological, and electrical properties which confirmed the different nature of the deposited films on the Silicon substrate. Decent hysteresis behavior has been confirmed by current-voltage (IV) measurement in all the four deposited films. The highest current has been measured for GaN at ～60 nA and the lowest current in SiC at ～30 nA level which is quite low comparing with the expected signal level (μA). The HVG technique is suitable to understand surface features of thin films which are substantially advantageous for the energy devices, detectors, optoelectronic devices operating at high temperatures.     

Material encapsulation in poly(methyl methacrylate) shell: A review

Material encapsulation is a relatively new technique for coating a micro/nanosize particle or droplet with polymeric or inorganic shell. Encapsulation technology has many applications in various fields including drug delivery, cosmetic, agriculture, thermal energy storage, textile, and self-healing polymers. Poly(methyl methacrylate) (PMMA) is widely used as shell material in encapsulation due to its high chemical stability, biocompatibility, nontoxicity, and good mechanical properties. The main approach for micro/nanoencapsulation of materials using PMMA as shell comprises emulsion-based techniques such as emulsion polymerization and solvent evaporation from oil-in-water emulsion. In the present review, we first focus on the encapsulation techniques of liquid materials with PMMA shell by analyzing the effective processing parameters influencing the preparation of PMMA micro/nanocapsules. We then describe the morphology of PMMA capsules in emulsion systems according to thermodynamic relations. The techniques to investigation of mechanical properties of capsule shell and the release mechanisms of core material from PMMA capsules were also investigated. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48039.     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

Microstructural characterization of plasma sprayed Ln2Zr2O7 thermal barrier coatings by electron microscopy methods

The presented article characterized microstructural aspects of thermal barrier coatings (TBCs) analysis using methods of electron microscopy such as electron backscatter diffraction (EBSD), transmission/scanning electron microscopy (S/TEM), and TEM. The analyzed TBC system is based on gadolinium zirconate deposited by air plasma spraying method, and additionally, it was subjected to an oxidation test for 500 hr at a temperature of 1,100°C. Moreover, the morphological characterization of feedstock powder was showed. EBSD analysis revealed the inhomogeneity of feedstock materials in the form of complex phase composition. In the case of deposited coating, this method was used to characterize the crystallite size of zirconate coating and phase composition of thermally grown oxide zone. S/TEM and TEM analysis showed morphological details of this zone but not revealed such phase as perovskite oxide of GdAlO₃ type.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.