Red Bank, NJ, United States
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Romanski F.S.,Rutgers Chemical and Biochemical Engineering | Dubey A.,Rutgers Chemical and Biochemical Engineering | Chester A.W.,Rutgers Chemical and Biochemical Engineering | Tomassone M.S.,Rutgers Chemical and Biochemical Engineering
Powder Technology | Year: 2012

The dry impregnation of catalysts is widely used in industrial catalyst preparation, however, until recently, it has not been possible to model this system computationally. In this work, a novel algorithm for the spray and inter-particle transfer of fluid onto and within a rotating bed of granular catalyst support was explored using discrete element method (DEM). The simulations were validated by experiments utilizing a geometrically identical double cone blender fixed with a single nozzle impregnator. The effects of liquid flow rate and fill level were explored at a fixed rotation rate of 25. rpm. Specifically, three flow rates of 1.5, 2.5 and 5. L/h were selected and evaluated at a 30% and 45% fill fraction by volume. Mixing analysis and fluid concentration distributions were used both experimentally and computationally to investigate the propagation of fluid throughout the bed with the goal of modeling and improving industrial content uniformity. It was discovered that low flow rate systems and lower fill fractions resulted in better mixing and content uniformity throughout the bed. Results obtained from our model show good agreement with experiments, and therefore it was demonstrated that a novel fluid transfer algorithm incorporated into DEM could be used to accurately model dry catalyst impregnation, therefore introducing a new tool for optimizing catalyst manufacturing. © 2011 Elsevier B.V.

Zhu W.,Rutgers Chemical and Biochemical Engineering | Romanski F.S.,Rutgers Chemical and Biochemical Engineering | Dalvi S.V.,New Jersey Institute of Technology | Dave R.N.,New Jersey Institute of Technology | Silvina Tomassone M.,Rutgers Chemical and Biochemical Engineering
Chemical Engineering Science | Year: 2012

The production and stabilization of crystalline pharmaceutical suspensions, an area of great importance to the pharmaceutical industry, was investigated through exploration of the ability of surfactants and polymers to affect the growth and stability of poorly water soluble pharmaceutical crystals on a molecular scale using molecular dynamics simulations coupled with anti-solvent crystallizations. Griseofulvin, a model poorly soluble drug, was simulated in an aqueous system containing individual non-ionic surfactant Tween 80, polymer HPMC, polymer Pullulan, and anionic surfactant SDS, as well as binary surfactant-polymer mixtures. To evaluate the stability and growth potential of the crystals, the interfacial binding energies between three elementary faces were calculated and compared, based on which HPMC was determined to be the most effective individual additive of those tested. Additionally, the potential synergism of additives was evaluated by the simulation of binary mixtures containing each of the non-ionic additives with the anionic surfactant SDS at different starting configurations. The combination of HPMC with SDS proved to be the most effective and significantly greater than HPMC when used alone. Simulation results were validated by observing both crystal growth rates, as well as final particle sizes of griseofulvin crystals grown using an antisolvent crystallization in the presence of individual and mixtures of additives, suggesting that the simulation approach may be used as a screening tool for selection of additives. © 2012 Elsevier Ltd.

PubMed | Rutgers Chemical and Biochemical Engineering
Type: Journal Article | Journal: European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences | Year: 2011

It is currently of great interest to the pharmaceutical industry to control the size and agglomeration of nano- and micro-particles for the enhancement of drug delivery. Typically, surfactants and polymers are used as additives to interact with and stabilize the growing crystal surface, thus controlling size and agglomeration; however, selection is traditionally done empirically or using heuristics. The objective of this study was to use molecular dynamic simulations to investigate and predict additive interactions, and thus, evaluate the stabilization potential of individual and multiple additives on the surface of the model drug fenofibrate. Non-ionic surfactant Tween 80, anionic surfactant sodium dodecyl sulfate (SDS), and polymers hydroxypropyl methylcellulose (HPMC) and Pullulan were evaluated individually on three distinct crystal surfaces [(001), (010), (100)], as well as in surfactant-polymer combinations. HPMC was determined to have the strongest interaction with the surfaces of the fenofibrate crystal, and therefore, was predicted to be the most effective individual additive. A mixture of HPMC with SDS was determined to be the most effective mixture of additives, and more effective than HPMC alone, indicating a synergistic effect. The predictions of mixed additives indicated a relative order of effectiveness as follows: HPMC-SDS>HPMC-Tween 80>Pullulan-Tween 80>Pullulan-SDS. The simulations were subsequently validated by an anti-solvent crystallization of fenofibrate where it was found that HPMC individually, and a mixture of HPMC-SDS, produced the smallest and most stable crystals, as measured by laser diffraction; this, in combination with measurements of the crystal growth rate in the presence and absence of additives confirmed the results of the simulations.

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