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Coral-like TiO 2 nanostructured films were chemically synthesized through the sol−gel method for fabrication of dye-sensitized solar cell (DSSC). The influence of experimental parameters such as precursor hydrolysis rate, reaction time, type and concentration of acid, and annealing temperature was studied through analysis of the surface structure of the films. The corallike TiO 2 film has excellent light scattering property and a mesoporous structure with fairly large specific surface area of 164 m 2 g −1 . The resulting DSSC, which consists of a dense, coral-like TiO 2 nanostructured film and the dye N719 in an electrolyte, shows a better performance as compared to a fabricated DSSC by using commonly used TiO 2 . A photocurrent value of approximately 16.1 mA cm −2 , a fill factor of 77.6%, and a conversion efficiency of 9.4% were obtained. The low cost and the possibility of controlling the morphology of the prepared film make this method an interesting candidate for use in fabricating photovoltaic devices.

The hydrodynamic performance of micrometric TiO 2 particles has been experimentally studied in a conical fluidized bed and the results compared with numerical simulations. Local solid velocities in the bed have been measured by means of an optical fiber technique under different operating conditions of particle loading and air velocity. The radial profiles of axial solid velocities have been simulated to assess the sensitivity of grid size, and different drag models, namely, those by Syamlal and O'Brien, Ahmadi and Ma, Arastoopour et al., and Gidaspow, for no-slip, partial-slip, and free-slip boundary conditions (BCs). The different drag models record almost similar results, but those provided by the Gidaspow and Ahmadi-Ma models, together with free-slip BCs, are in somewhat better agreement with the experimental data for conical fluidized beds with smooth walls. V C 2013 American Institute of Chemical Engineers AIChE J, 59: [4502][4503][4504][4505][4506][4507][4508][4509][4510][4511][4512][4513][4514][4515][4516][4517][4518] 2013 Keywords: conical fluidized bed, solid velocity, slip boundary conditions, numerical simulation, drag models IntroductionFluidized and spouted beds are extensively used for various industrial applications in pharmaceuticals, 1,2 drying, 3,4 food processing, 5 combustion, 6,7 pyrolysis, and catalytic polymerization. 8,9 A conical fluidized bed has the characteristics of both fluidized and spouted beds, being, therefore, suitable for handling agglomerating or sticky powders. This is particularly the case for the fluidization of fine particles with a wide size distribution corresponding to different groups of Geldart's classification. 10,11 Although many studies have been reported in the literature involving fluidized beds, [12][13][14][15] certain details of the hydrodynamic performance of conical fluidized beds are not fully understood, which is partially due to the few experimental data on the hydrodynamics of these beds. 16,17 Accordingly, the design and scaling-up of conical fluidized beds is not straightforward, given that bed geometry, the ratio of inlet diameter to bed height and operating conditions highly influence gas-solid flow regime. Furthermore, hydrodynamic parameters such as pressure drop, solid volume fraction, and velocity significantly affect the performance of the conical bed. Optimizing the performance of conical fluidized beds requires a thorough understanding of the mixing characteristics and interactions between the gas and solid phases. The standard noninvasive measurement techniques used for the determination of particle trajectories and velocities are as follows: pitot tube, 18 Laser-Doppler anemometry, 19 video technique, 20 radioactive particle tracking, 21 and particle image velocimetry. 22 Nevertheless, techniques based on an optical-fiber probe are also promising for this purpose. [23][24][25][26][27][28][29] In recent years, with the development of high performance computers, researchers have used numerical methods to study the flow behavior of fluidized...

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Effects of the deposition process parameters on the thickness of TiO 2 nanostructured film were simulated using the molecular dynamics (MD) approach and modeled by the artificial neural network (ANN) and regression method. Accordingly, TiO 2 nanostructured film was prepared experimentally with the sol-gel dip-coating method. Structural instabilities can be expected, due to short-and/or long-range intermolecular forces, leading to the surface inhomogeneities. In the MD simulation, the Morse potential function was used for the inter-atomic interactions, and equations of motion for atoms were solved by Verlet algorithm. The effect of the withdrawal velocity, drying temperature and number of deposited layers were studied in order to characterize the film thickness. The results of MD simulations are reasonably consistent with atomic force microscopy, scanning electron microscopy and Dektak surface profiler. Finally, the outputs from experimental data were analyzed by using the ANN in order to investigate the effects of deposition process parameters on the film thickness. In this case, various architectures have been checked using 75% of experimental data for training of the ANN. Among the various architectures, feed-forward back-propagation network with trainer training algorithm was found as the best architecture. Based on the R-squared value, the ANN is better than the regression model in predicting the film thickness. The statistical analysis for those results was then used to verify the fitness of the complex process model. Based on the results, this modeling methodology can explain the characteristics of the TiO 2 nanostructured thin film and growth mechanism varying with process conditions.

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