Characterization and selection of suitable grades of lactose as functional fillers for capsule filling: part 1

The purpose of this work is to characterize thermal, physical and mechanical properties of different grades of lactose and better understand the relationships between these properties and capsule filling performance. Eight grades of commercially available lactose were evaluated: Pharmatose 110?M, 125?M, 150?M, 200?M, 350?M (α-lactose monohydrate), AL (anhydrous lactose containing ～80% β-AL), DCL11 (spray dried α-lactose monohydrate containing ～15% amorphous lactose) and DCL15 (granulated α-lactose monohydrate containing ～12% β-AL). In this study, different lactose grades were characterized by thermal, solid state, physical and mechanical properties and later evaluated using principal component analysis (PCA) to assess the inter-relationships among some of these properties. The lactose grades were characterized by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray diffraction (XRD), moisture sorption/desorption isotherms, particle size distribution; the flow was characterized by Carr Index (CI), critical orifice diameter (COD) and angle of friction. Plug mechanical strength was estimated from its diametric crushing strength. The first and second principal components (PC) captured 47.6% and 27.4% of variation in the physical and mechanical property data, respectively. The PCA plot grouped together 110?M, AL, DCL11 and DCL15 on the one side of plot which possessed superior properties for capsule formulation and these grades were selected for future formulation development studies (part II of this work).     

Thermal Upgrading of Nickeliferous Pyrrhotite Tailings for the Recovery of Nickel in the Form of Ferronickel Alloy

Metallurgical and Materials Transactions B - Production of ferronickel alloy by thermal treatment of nickeliferous pyrrhotite (Pyrr) tailings was studied by both thermodynamic assessment and...     

Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons

Central Force Optimization (CFO) is a novel and upcoming metaheuristic technique that is based upon physical kinematics. It has previously been demonstrated that CFO is effective when compared with other metaheuristic techniques when applied to multiple benchmark problems and some real world applications. This work applies the CFO algorithm to training neural networks for data classification. As a proof of concept, the CFO algorithm is first applied to train a basic neural network that represents the logical XOR function. This work is then extended to train two different neural networks in order to properly classify members of the Iris data set. These results are compared and contrasted to results gathered using Particle Swarm Optimization (PSO) in the same applications. Similarities and differences between CFO and PSO are also explored in the areas of algorithm design, computational complexity, and natural basis. The paper concludes that CFO is a novel and promising meta-heuristic that is competitive with if not superior to the PSO algorithm, and there is much room to further improve it.     

Current and Temperature Dependent Matching Fields in Nb Film of Rectangular Array of Holes

Nanoengineered periodic array of holes on superconducting thin films have a great interest due to their excellence for the studies of the vortex pinning mechanisms in the type-II superconductors. Rectangular array of holes has been fabricated over a microbridge of Nb superconducting thin film by e-beam lithography. Rectangular array of holes have two type of scenario, at low magnetic fields matching effects are sharp and narrow while at high fields wide and shallower. In this work, we study the matching pinning effect by the artificial hole array in superconducting Nb thin films. We observed that as the inter distance between holes is decreased; the sharp matching effects become dominant and vice versa.     

Hydrothermal synthesis of different colors and morphologies of ZnO nanostructures and comparison of their photocatalytic properties

Various zinc oxide nanostructures were synthesized using thermal decomposition of zinc acetate dihydrate in a single process. The characterization of samples using powder X-ray diffraction, scanning electron microscope and FT-IR measurements revealed that the pure phase of different morphologies such as nanoparticles, nanowires and nanodisks had been synthesized successfully. Surprisingly some synthesized ZnO nanostructures were dark gray. The results showed that the reason may have been related to the oxygen deficiency and strong asymmetric stretching mode of wurtzite ZnO nanostructure. Using such samples, the photodegradation of Methylene blue was performed by UV–vis absorption measurement and the effect of morphology on the photocatalytic properties of different ZnO nanostructures was examined. The results showed that the nanodisks had the best photocatalytic performance among the other morphologies. The reason was attributed to the presence of specific crystal planes such as (0001) facets in nanodisks which can improve their photocatalytic performance.     

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance.

     

Purification of metallurgical silicon using iron as an impurity getter part I: Growth and separation of Si

The purification of metallurgical grade silicon (MG-Si) using a combination of solvent refining and physical separation is studied. MG-Si was alloyed with iron and solidified under different cooling rates in order to grow pure Si dendrites from the alloy. The Si dendrites were then separated using a gravity-based method. The separation method relies on the significantly different densities of Si and FeSi₂, and it uses a heavy liquid with specific gravity between the two phases to float the light Si particles to the surface of the liquid, while the heavy iron silicide sinks. The effects of the particle size and cooling rate on the yield and separation efficiency of the Si phase were investigated by quantifying the fraction of Si in the sinks and floats. The results demonstrate that the crushing size of the particles prior to separation should be approximately the same as the width of the dendrites in order to maximize the separation efficiency while simultaneously lowering the grinding cost.