Characterization of palladium supported on sulfated zirconia catalysts by DRIFTS, XAS and n-butane isomerization reaction in the presence of hydrogen

Palladium supported on sulfated zirconia (PdSZ) has been characterized by the n-butane isomerization reaction in the presence of hydrogen, X-ray absorption spectroscopy (XAS) and diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) of adsorbed carbon monoxide. Catalyst calcination at 873 K followed by hydrogen reduction at 513 K results in the formation of 30–40 Å Pd metal clusters, but the surface can only weakly adsorb CO, though stronger than Pd-free, sulfated zirconia catalysts. In the presence of hydrogen, PdSZ has a lower n-butane isomerization activity than SZ, and the Pd function cannot stabilize the reaction at low H₂/n-butane ratios.     

Printability of elastomer latex for additive manufacturing or 3D printing

Additive manufacturing, sometimes referred to as 3D printing is a new, rapidly developing technology which has the potential to revolutionize fabrication of certain high value, complex products. Until now conventional elastomers have not been widely used in the additive manufacturing process. The goal of our work was to determine the feasibility of additive manufacturing using ink jet printing of elastomeric latex materials. Particle size, viscosity, and surface tension were measured for five different latex materials—poly(2‐chloro‐1,3‐butadiene), carboxylated styrene‐butadiene rubber, carboxylated butadiene‐acrylonitrile copolymer, natural rubber, and prevulcanized natural rubber. The XSBR latex was predicted as the one most likely to be printable. Printing trials carried out with the XSBR as the ink proved it to be printable, although technical problems of agglomeration and print head clogging need to be addressed and both the material and process need to be optimized for consistent printing to be achieved. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 42931.     

Mechanical Behavior of MoSi2 Reinforced–Si3N4Matrix Composites

The mechanical behavior of MoSi₂ reinforced–Si₃N₄ matrix composites was investigated as a function of MoSi₂ phase content, MoSi₂ phase size, and amount of MgO densification aid for the Si₃N₄ phase. Coarse-phase MoSi₂-Si₃N₄ composites exhibited higher room-temperature fracture toughness than fine-phase composites, reaching values >8 MP·am^1/2. Composite fracture toughness levels increased at elevated temperature. Fine-phase composites were stronger and more creep resistant than coarse phase composites. Room-temperature strengths >1000 MPa and impression creep rates of ∼10⁻⁸ s⁻¹ at 1200°C were observed. Increased MgO levels generally were deleterious to MoSi₂-Si₃N₄ mechanical properties. Internal stresses due to MoSi₂ and Si₃N₄ thermal expansion coefficient mismatch appeared to contribute to fracture toughening in MoSi₂-Si₃N₄ composites.     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.     

Decision support systems in water resources in the demarcated region of Douro – case study in Pinhão river basin,Portugal

This paper puts forward the importance of decision support systems (DSS) for the planning and management of water resources. A case study of the Pinhão river basin, in Portugal, is presented. Given the importance of vineyards in the Douro region, it is important to determine if water availability will be enough to meet present and future water demands. In order to answer this question, DSS tools were used to assess different scenarios. The MIKE BASIN software was used in the hydrographical basin of Pinhão river, assisted by a geographic information system, GIS, which allowed the modelling of the basin, both temporally and spatially, facilitating the visualisation and interpretation of results. According to the attained results, it was verified that, at present, the hydrographical basin meets the considered water needs. The DSS proved to be an important tool to assist the decision‐making process in the studied river basin.     

Microscopic diagnosis of the leaf and stem of Piper solmsianum C.DC

Piper solmsianum C.DC., which is popularly known as pariparoba, is a shrub that measures 1–3 m in height and it inhabits areas with wet tropical soils. The objective of this study was to analyze the leaf and stem anatomy using light microscopy, scanning electron micrographs, and energy‐dispersive X‐ray spectroscopy in order to provide information for species identification. The anatomical profile showed the following main microscopic markers: hypostomatic leaf; hypodermis layer on both sides; pearl glands; biconvex midrib shape; five collateral vascular bundles in open arc with the central bundle larger than the others; circular stem shape; collateral vascular bundles arranged in two rings; sinuous sclerenchymatic sheath in the pith; secretory idioblasts; and starch grains in the mesophyll, in the ground parenchyma of the midrib, petiole, and in the stem; and six morphotypes of calcium oxalate crystals (styloids, cuneiform, tabular crystal rosettes, cuneiform crystal rosettes, elongated square dipyramids, as well as very elongated square dipyramids).     

Statistical Regularization for Identification of Structural Parameters and External Loadings Using State Space Models

A novel numerical approach is presented, in the time domain, to simultaneously identify structural parameters and unmeasured input loadings using incomplete output measurement only. The identification problem is formulated as an optimization process, wherein the objective function is defined as the discrepancy between the measured and the predicted data, and is solved by a damped Gauss‐Newton method. Because the proposed algorithm is a time domain technique, forward analyses are required to obtain predicted system responses so as to compute the discrepancy. Therefore, we propose an input force estimation scheme in the identification process to complete the task of input‐output forward analyses, for the case of output‐only measurement. The relationship between the unknown input loadings and the output measurement is established through a state space model, which basically formulates an ill‐posed least squares problem. A statistical Bayesian inference‐based regularization technique is presented to solve such a least squares problem. Finally, the proposed approach is illustrated by both numerical and experimental examples using output‐only measurements of either acceleration or strain time histories. The results clearly show the robustness and the applicability of the proposed algorithm to simultaneously identify structural parameters and unmeasured input loadings with a high accuracy.     

Crop yields and soil organic carbon fractions as influenced by straw incorporation in a rice–wheat cropping system in southeastern China

In agro-ecosystems, the relationship between soil fertility and crop yield is mediated by manure application. In this study, an 8-year field experiment was performed with four fertilizer treatments (NPK, NPKM₁, NPKM₂, and NPKM₃), where NPK refers to chemical fertilizer and M₁, M₂, and M₃ refer to manure application rates of 15, 30, and 45 Mg ha^?1 year^?1, respectively. The results showed that the NPKM (NPKM₁, NPKM₂, and NPKM₃) treatments produced greater and more stable yields (4.95–5.45 Mg ha^?1 and 0.59–0.75) than the NPK treatment (4.01 Mg ha^?1 and 0.50). Crop yields under the NPKM treatments showed two trends, with a rate of decrease of 0.48–0.83 Mg ha^?1 year^?1 during the first 4 years and a rate of increase of 0.10–0.25 Mg ha^?1 year^?1 during the last 4 years. The soil organic carbon (SOC) significantly increased under all treatments. The estimated annual SOC decomposition rate was 0.35 Mg ha^?1 year^?1 and the equilibrium SOC level was 6.22 Mg ha^?1. Soil total nitrogen (N), available N, total phosphorus (P) and available P under the NPKM treatments increased by 0.15–0.26, 15–33, 0.17–0.66 and 45–159 g kg^?1, respectively, compared with the NPK treatment. Manure application mainly influenced crop yield by affecting the soil TN, available N, and available P, which accounted for up to 64% of the crop yield variation. Taken together, applying manure can determine or at least improve the effects of soil fertility on crop yield in acidic soils in South China.     

Catalytic olefin polymerisation at short times: Studies using specially adapted reactors

Despite the fact that the very early stages (several tens of seconds) of catalysed olefin polymerisation processes appear negligibly short with respect to the residence time of most industrial reactors, they are critical in terms of catalyst activation, obtaining good particle morphology, and avoiding irreparable problems caused by particle overheating. The different types of reactors that have been used over the course of the past few years are discussed in this feature article. It is shown that despite the difficulties encountered in finding the perfect experimental tool for this purpose, different configurations of stopped flow reactors can be used successfully to explore different aspects of what happens to the catalyst (supported and molecular) during these critical moments of polymerisation. © 2012 Canadian Society for Chemical Engineering