Virus-sized colloid transport in a single pore: Model development and sensitivity analysis

A mathematical model is developed to simulate the transport and deposition of virus-sized colloids in a cylindrical pore throat considering various processes such as advection, diffusion, colloid-collector surface interactions and hydrodynamic wall effects. The pore space is divided into three different regions, namely, bulk, diffusion and potential regions, based on the dominant processes acting in each of these regions. In the bulk region, colloid transport is governed by advection and diffusion whereas in the diffusion region, colloid mobility due to diffusion is retarded by hydrodynamic wall effects. Colloid-collector interaction forces dominate the transport in the potential region where colloid deposition occurs. The governing equations are non-dimensionalized and solved numerically. A sensitivity analysis indicates that the virus-sized colloid transport and deposition is significantly affected by various pore-scale parameters such as the surface potentials on colloid and collector, ionic strength of the solution, flow velocity, pore size and colloid size. The adsorbed concentration and hence, the favorability of the surface for adsorption increases with: (i) decreasing magnitude and ratio of surface potentials on colloid and collector, (ii) increasing ionic strength and (iii) increasing pore radius. The adsorbed concentration increases with increasing Pe, reaching a maximum value at Pe = 0.1 and then decreases thereafter. Also, the colloid size significantly affects particle deposition with the adsorbed concentration increasing with increasing particle radius, reaching a maximum value at a particle radius of 100 nm and then decreasing with increasing radius. System hydrodynamics is found to have a greater effect on larger particles than on smaller ones. The secondary minimum contribution to particle deposition has been found to increase as the favorability of the surface for adsorption decreases. The sensitivity of the model to a given parameter will be high if the conditions are favorable for adsorption. The results agree qualitatively with the column-scale experimental observations available in the literature. The current model forms the building block in upscaling colloid transport from pore scale to Darcy scale using Pore-Network Modeling. (C) 2014 Elsevier By. All rights reserved.

Biocompatibility property of 100% strontium-substituted SiO2-Al2O3-P2O5-CaO-CaF2 glass ceramics over 26 weeks implantation in rabbit model: Histology and micro-Computed Tomography analysis

One of the desired properties for any new biomaterial composition is its long-term stability in a suitable animal model and such property cannot be appropriately assessed by performing short-term implantation studies. While hydroxyapatite (HA) or bioglass coated metallic biomaterials are being investigated for in vivo biocompatibility properties, such study is not extensively being pursued for bulk glass ceramics. In view of their inherent brittle nature, the implant stability as well as impact of long-term release of metallic ions on bone regeneration have been a major concern. In this perspective, the present article reports the results of the in vivo implantation experiments carried out using 100% strontium (Sr)-substituted glass ceramics with the nominal composition of 4.5 SiO2-3Al(2)O(3)-1.5P(2)O(5)-3SrO-2SrF(2) for 26 weeks in cylindrical bone defects in rabbit model. The combination of histological and micro-computed tomography analysis provided a qualitative and quantitative understanding of the bone regeneration around the glass ceramic implants in comparison to the highly bioactive HA bioglass implants (control). The sequential polychrome labeling of bone during in vivo osseointegration using three fluorochromes followed by fluorescence microscopy observation confirmed homogeneous bone formation around the test implants. The results of the present study unequivocally confirm the long-term implant stability as well as osteoconductive property of 100% Sr-substituted glass ceramics, which is comparable to that of a known bioactive implant, that is, HA-based bioglass. (c) 2014 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 103B: 1168-1179, 2015.

Anthropogenic aerosol fraction over the Indian region: model simulations versus multi-satellite data analysis

Anthropogenic aerosols play a crucial role in our environment, climate, and health. Assessment of spatial and temporal variation in anthropogenic aerosols is essential to determine their impact. Aerosols are of natural and anthropogenic origin and together constitute a composite aerosol system. Information about either component needs elimination of the other from the composite aerosol system. In the present work we estimated the anthropogenic aerosol fraction (AF) over the Indian region following two different approaches and inter-compared the estimates. We espouse multi-satellite data analysis and model simulations (using the CHIMERE Chemical transport model) to derive natural aerosol distribution, which was subsequently used to estimate AF over the Indian subcontinent. These two approaches are significantly different from each other. Natural aerosol satellite-derived information was extracted in terms of optical depth while model simulations yielded mass concentration. Anthropogenic aerosol fraction distribution was studied over two periods in 2008: premonsoon (March-May) and winter (November-February) in regard to the known distinct seasonality in aerosol loading and type over the Indian region. Although both techniques have derived the same property, considerable differences were noted in temporal and spatial distribution. Satellite retrieval of AF showed maximum values during the pre-monsoon and summer months while lowest values were observed in winter. On the other hand, model simulations showed the highest concentration of AF in winter and the lowest during pre-monsoon and summer months. Both techniques provided an annual average AF of comparable magnitude (similar to 0.43 +/- 0.06 from the satellite and similar to 0.48 +/- 0.19 from the model). For winter months the model-estimated AF was similar to 0.62 +/- 0.09, significantly higher than that (0.39 +/- 0.05) estimated from the satellite, while during pre-monsoon months satellite-estimated AF was similar to 0.46 +/- 0.06 and the model simulation estimation similar to 0.53 +/- 0.14. Preliminary results from this work indicate that model-simulated results are nearer to the actual variation as compared to satellite estimation in view of general seasonal variation in aerosol concentrations.

Gasification of wood particles in a co-current packed bed: Experiments and model analysis

This study focuses on addressing the propagation front movement in a co-current downdraft gasification system. A detailed single particle modeling analysis extended to the packed bed reactor is used to compare with the experimental measurement as well those available in the literature. This model for biomass gasification systems considered pyrolysis process, gas phase volatile combustion, and heterogeneous char reactions along with gas phase reactions in the packed bed. The pyrolysis kinetics has a critical influence on the gasification process. The propagation front has been shown to increase with air mass flux, attains a peak and then decreases with further increase in air mass flux and finally approaches negative propagation rate. This indicates that front is receding, or no upward movement() bra her it is moving downward towards the char bed. The propagation rate correlates with mass flux as (m) over dot `'(0.883) during the increasing regimes of the front movement The study clearly identifies that bed movement is an important parameter for consideration in a co-current configuration towards establishing the effective bed movement. The study also highlights the importance of surface area to volume ratio of the particles in the packed bed and its influence on the volatile generation. Finally, the gas composition for air gasification under various air mass fluxes is compared with the experimental results. (C) 2016 Elsevier B.V. All rights reserved.