42 resultados para particle dispersion


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This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series, but also determines the best neural network architecture. An experimental analysis is performed using four real time series and the results are shown in terms of six performance measures. The experimental results demonstrate that the proposed methodology achieves a fair prediction of the presented pollutant time series by using compact networks.

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In this study, the concentration and morphological characteristics of inhalable particulate material (PM10) were evaluated and associated with climatic conditions. The mean annual concentration was 11.0 µg m−3, varying between 0,647 µg m−3 and 36.8 µg m−3. Wind speed has a higher influence on PM10 dispersion, but direction was associated with particle source. During the wet period, wind speed is the main dispersion factor, while speed and direction both are important during the dry period. Based on the morphological characteristics, it is concluded that biogenic particles prevail during the rainy season and terrigenous particles during the dry period, depending on the wind direction and intensity.

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Ni-Co/Al2O3-MgO-ZrO2 nanocatalyst with utilization of two different zirconia precursors, namely, zirconyl nitrate hydrate (ZNH) and zirconyl nitrate solution (ZNS), was synthesized via the sol-gel method. The physiochemical properties of nanocatalysts were characterized by XRD, FESEM, EDX, BET and FTIR analyses and employed for syngas production from CO2-reforming of CH4. XRD patterns, exhibiting proper crystalline structure and homogeneous dispersion of active phase for the nanocatalyst ZNS precursor employed (NCAMZ-ZNS). FESEM and BET results of NCAMZ-ZNS presented more uniform morphology and smaller particle size and consequently higher surface areas. In addition, average particle size of NCAMZ-ZNS was 15.7 nm, which is close to the critical size for Ni-Co catalysts to avoid carbon formation. Moreover, FESEM analysis indicated both prepared samples were nanoscale. EDX analysis confirmed the existence of various elements used and also supported the statements made in the XRD and FESEM analyses regarding dispersion. Based on the excellent physiochemical properties, NCAMZ-ZNS exhibited the best reactant conversion across all of the evaluated temperatures, e.g. CH4 and CO2 conversions were 97.2 and 99% at 850 ºC, respectively. Furthermore, NCAMZ-ZNS demonstrated a stable yield with H2/CO close to unit value during the 1440 min stability test.

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An evaluation of the pesticides extracted from the soil matrix was conducted using a citrate-buffered solid phase dispersion sample preparation method (QuEChERS). The identification and quantitation of pesticide compounds was performed using gas chromatography-mass spectrometry. Because of the occurrence of the matrix effect in 87% of the analyzed pesticides, the quantification was performed using matrix-matched calibration. The method's quantification limits were between 0.01 and 0.5 mg kg-1. Repeatability and intermediate precision, expressed as a relative standard deviation percentage, were less than 20%. The recoveries in general ranged between 62% and 99%, with a relative standard deviation < 20%. All the responses were linear, with a correlation coefficient (r) ≥0.99.

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This work reports on the investigation of nanosized CeO2-ZnO systems prepared by Pechini's method. The structural and morphological characterization of CeO2-ZnO systems as well as the characterization of CeO2 and ZnO separately, showed that the employed method result in powders with spheroidal particles whose size are in the range 30 - 200 nm, which is appropriate to provide homogeneous suspensions. The ZnO present in the prepared mixed oxides seems to increase particle size distribution and to influence the arrangement of the particles after powder dispersion.

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Information about capacity of transport and dispersion of soluble pollutants in natural streams are important in the management of water resources, especially in planning preventive measures to minimize the problems caused by accidental or intentional waste, in public health and economic activities that depend on the use of water. Considering this importance, this study aimed to develop a warning system for rivers, based on experimental techniques using tracers and analytical equations of one-dimensional transport of soluble pollutants conservative, to subsidizing the decision-making in the management of water resources. The system was development in JAVA programming language and MySQL database can predict the travel time of pollutants clouds from a point of eviction and graphically displays the temporal distribution of concentrations of passage clouds, in a particular location, downstream from the point of its launch.

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A non isotropic turbulence model is extended and applied to three dimensional stably stratified flows and dispersion calculations. The model is derived from the algebraic stress model (including wall proximity effects), but it retains the simplicity of the "eddy viscosity" concept of first order models. The "modified k-epsilon" is implemented in a three dimensional numerical code. Once the flow is resolved, the predicted velocity and turbulence fields are interpolated into a second grid and used to solve the concentration equation. To evaluate the model, various steady state numerical solutions are compared with small scale dispersion experiments which were conducted at the wind tunnel of Mitsubishi Heavy Industries, in Japan. Stably stratified flows and plume dispersion over three distinct idealized complex topographies (flat and hilly terrain) are studied. Vertical profiles of velocity and pollutant concentration are shown and discussed. Also, comparisons are made against the results obtained with the standard k-epsilon model.

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Fiber reinforced polymer composites have been used in many applications, such as in automobile, aerospace and naval industries, due basically to their high strength-to-weight and modulus-to-weight, among other properties. Even though particles are usually not able to lead to the level of reinforcement of fibers, particle reinforced polymer composites have been proposed for many new applications due to their low cost, easy fabrication and isotropic properties. In this work, polymer composites were prepared by incorporating glass particles of different morphologies on poly(aryl sulfones) matrices. Particles with aspect ratios equal to 1, 2.5 and 10 were used. The prepared composites were characterized using electron microscopy and thermal analysis. Mechanical properties of the composites were evaluated using a four-point bending test. The thermo-mechanical behavior of the obtained composites was also investigated. The results showed that the morphology of the particles alter significantly the mechanical properties of composites. Particles with larger values of aspect ratio led to large elastic modulus but low levels of strain at failure. This result was explained by modeling the thermo-mechanical behavior of the composites using a viscoelastic model. Parameters of the model, obtained from a Cole-Cole type of plot, demonstrated that interactions at the polymer-reinforcing agent interface were higher for composites with large aspect ratio particles. Higher levels of interactions at interfaces can lead to higher degrees of stress transfer and, consequently, to composites with large elastic modulus, as experimentally observed.

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Reliable predictions of remaining lives of civil or mechanical structures subjected to fatigue damage are very difficult to be made. In general, fatigue damage is extremely sensitive to the random variations of material mechanical properties, environment and loading. These variations may induce large dispersions when the structural fatigue life has to be predicted. Wirsching (1970) mentions dispersions of the order of 30 to 70 % of the mean calculated life. The presented paper introduces a model to estimate the fatigue damage dispersion based on known statistical distributions of the fatigue parameters (material properties and loading). The model is developed by expanding into Taylor series the set of equations that describe fatigue damage for crack initiation.

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Wear particles are phagocytosed by macrophages and other inflammatory cells, resulting in cellular activation and release of proinflammatory factors, which cause periprosthetic osteolysis and subsequent aseptic loosening, the most common causes of total joint arthroplasty failure. During this pathological process, tumor necrosis factor-alpha (TNF-α) plays an important role in wear-particle-induced osteolysis. In this study, recombination adenovirus (Ad) vectors carrying both target genes [TNF-α small interfering RNA (TNF-α-siRNA) and bone morphogenetic protein 2 (BMP-2)] were synthesized and transfected into RAW264.7 macrophages and pro-osteoblastic MC3T3-E1 cells, respectively. The target gene BMP-2, expressed on pro-osteoblastic MC3T3-E1 cells and silenced by the TNF-α gene on cells, was treated with titanium (Ti) particles that were assessed by real-time PCR and Western blot. We showed that recombinant adenovirus (Ad-siTNFα-BMP-2) can induce osteoblast differentiation when treated with conditioned medium (CM) containing RAW264.7 macrophages challenged with a combination of Ti particles and Ad-siTNFα-BMP-2 (Ti-ad CM) assessed by alkaline phosphatase activity. The receptor activator of nuclear factor-κB ligand was downregulated in pro-osteoblastic MC3T3-E1 cells treated with Ti-ad CM in comparison with conditioned medium of RAW264.7 macrophages challenged with Ti particles (Ti CM). We suggest that Ad-siTNFα-BMP-2 induced osteoblast differentiation and inhibited osteoclastogenesis on a cell model of a Ti particle-induced inflammatory response, which may provide a novel approach for the treatment of periprosthetic osteolysis.

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Spray drying is an important method used by the food industry in the production of microencapsulated flavors to improve handling and dispersion properties. The objective of this study was to evaluate the influence of the process conditions on the properties of rosemary essential oil microencapsulated by spray drying using gum Arabic as encapsulant. The effects of the wall material concentration (10-30%), inlet air temperature (135-195 ºC), and feed flow rate (0.5-1.0 L.h-1) on the moisture content, hygroscopicity, wettability, solubility, bulk and tapped densities, particle density, flowability, and cohesiveness were evaluated using a 2³ central composite rotational experimental design. Moisture content, hygroscopicity and wettability were significantly affected by the three factors analyzed. Bulk density was positively influenced by the wall material concentration and negatively by the inlet air temperature. Particle density was influenced by the wall material concentration and the inlet air temperature variables, both in a negative manner. As for the solubility, tapped density, flowability, and cohesiveness, the models did not fit the data well. The results indicated that moderate wall material concentration (24%), low inlet air temperature (135 ºC), and moderate feed flow rate (0.7 L.h-1) are the best spray drying conditions.

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Egg yolk was partially replaced (0, 25, 50, 75, and 100%) with octenyl succinic anhydride (OSA)-modified potato starch in a reduced-fat mayonnaise formulation to curtail the problems associated with high cholesterol and induced allergic reactions. The physicochemical properties included parameters such as: pH, fat content, and emulsion stability of the formulations analyzed. The samples with 75% and 100% egg yolk substitute showed the maximum emulsion stability (>95% after two of months storage), and they were selected according to cholesterol content, particle size distributions, dynamic rheological properties, microstructure, and sensory characteristic. A significant reduction (84-97%) in the cholesterol content was observed in the selected samples. Particle size analysis showed that by increasing the amount of OSA starch, the oil droplets with the peak size of 70 µm engulfed by this compound became larger. The rheological tests elucidated that in the absence of egg yolk, OSA starch may not result in a final product with consistent texture and that the best ratio of the two emulsifiers (OSA starch/egg yolk) to produce stable reduced-fat, low cholesterol mayonnaise is 75/25. The microscopic images confirmed the formation of a stable cohesive layer of starch surrounding the oil droplets emulsified in the samples selected.