Granulation in a Fluidized Bed II Effect of Binder Amount on the Final Granules

There are many parameters affecting the properties of the final granules prepared in a fluidized bed. In this study one of the product parameters, quantity of the binder, has been studied for its effect on the final granule size, size distribution and friability

Determination of granule size change as a function of binder quantity leaded us to study the growth mechanisms during fluidized bed granulation. Two mechanisms are suggested;

1) Snowballing of primary granules (nuclei)

2) Agglomeration of primary granules

It has been shown that there is a critical amount of binder at which the formation of the primary granules comes to an end if more binder is added to the system. Then granule growth occurs by agglomeration of the primary granules. The physical properties of the granules formed before and after this critical binder concentration varies significantly     

Quasistatic analysis of cylindrical coplanar waveguide with multilayer dielectrics

CAD-oriented analytic formulas are given for calculating the quasistatic parameters of the cylindrical CPW with three dielectrics. The effects of the dielectric materials on the quasistatic characteristics are investigated by using conformal mapping techniques. The calculated results are compared with those obtained for a corresponding planar CPW and a cylindrical CPW with a single dielectric. The curvature effects on the quasistatic characteristics are discussed. Moreover, the present expressions can also be extended to the cylindrical CPW with multilayer dielectrics. © 1998 John Wiley & Sons, Inc. Int J RF and Microwave CAE 8: 303–314, 1998.     

Weight Initialization for Simultaneous Recurrent Neural Network Trained with a Fixed-point Learning Algorithm

This letter presents a study of the Simultaneous Recurrent Neural network, an adaptive algorithm, as a nonlinear dynamic system for static optimization. Empirical findings, which were recently reported in the literature, suggest that the Simultaneous Recurrent Neural network offers superior performance for large-scale instances of combinatorial optimization problems in terms of desirable convergence characteristics improved solution quality and computational complexity measures. A theoretical study that encompasses exploration of initialization properties of the Simultaneous Recurrent Neural network dynamics to facilitate application of a fixed-point training algorithm is carried out. Specifically, initialization of the weight matrix entries to induce one or more stable equilibrium points in the state space of the nonlinear network dynamics is investigated and applicable theoretical bounds are derived. A simulation study to confirm the theoretical bounds on initial values of weights is realized. Theoretical findings and correlating simulation study performed suggest that the Simultaneous Recurrent Neural network dynamics possesses desirable stability characteristics as an adaptive recurrent neural network for addressing static optimization problems. This revised version was published online in June 2006 with corrections to the Cover Date.     

EUV lithographic radiation grafting of thermo-responsive hydrogel nanostructures

Nanostructures of the thermoresponsive poly(N-isopropyl acrylamide) (PNIPAAm) and of PNIPAAm-block-poly(acrylic acid) copolymers were produced on poly(tetrafluoroethylene-co-ethyelene) (ETFE) films using extreme ultraviolet (EUV) lithographic exposure with subsequent graft-polymerization. The phase transition of PNIPAAm nanostructures at the low critical solution temperature (LCST) at 32 °C was imaged by atomic force microscopy (AFM) phase contrast measurements in pure water. Results show a higher phase contrast for samples measured below the LCST temperature than for samples above the LCST, proving that the soft PNIPAAm hydrogel transforms into a much more compact conformation above the LCST. EUV lithographic exposures were combined with the reversible addition-fragment chain transfer (RAFT)-mediated polymerization using cyanoisopropyl dithiobenzoate (CPDB) as chain transfer agent to synthesize PNIPAAm block-copolymer nanostructures.     

Performance Comparison of 2.4 and 5 GHz WiFi Signals and Proposing a New Method for Mobile Indoor Positioning

Wireless Personal Communications - Nowadays, the location information of people and objects is a necessity for indoor environments. Therefore, many technologies, methods, and algorithms have been...     

A case study of a stress corrosion cracking failure in an AA5083 mold material used for curing rubber compounds

Protection of Metals and Physical Chemistry of Surfaces - In this study the failure analysis of an AA5083 mold material, used for curing rubber compounds, was carried out. The problem revealed...     

Super toughened and highly ductile PLA/TPU blend systems by in situ reactive interfacial compatibilization using multifunctional epoxy-based chain extender

This study investigates the influence of using multifunctional epoxy Joncryl ADR 4468 chain extender (CE) on the properties of various polylactide (PLA)/thermoplastic polyurethane (TPU) (75 wt/25 wt) blend systems. The blends were based on two different TPU grades with ether- and ester-based soft segment as the dispersed phase (i.e., TPUether and TPUester) and an amorphous and a semicrystalline PLA grades as the matrix (i.e., aPLA and scPLA). PLA appeared to be more compatible with the TPUester, which caused the enhancement of the impact strength and strain at break values of the blends more remarkably. The dynamic rheological experiments also confirmed that the CE revealed a better reactivity with TPUester than TPUether. This further enhanced the interfacial compatibility between the PLA and TPUester and thereby dramatically improved the impact strength and ductility of the PLA/TPUester blends, specifically those with 0.5 wt% CE. Meanwhile, aPLA as the matrix reflected the TPUs toughening effect more efficiently than scPLA. This was due to the possible shrinkage caused by the crystallization of scPLA matrix, which could deteriorate the interfacial interactions between the phases in the corresponding blends.     

Poly(2‐alkyloyloxyethylacrylate) and poly(2‐alkyloyloxyethylacrylate‐co‐methylacrylate) comblike polymers as novel phase‐change materials for thermal energy storage

A series of poly(2‐alkyloyloxyethylacrylate) and poly(2‐alkyloyloxyethylacrylate‐co‐methylacrylate) polymers as novel polymeric phase‐change materials (PCMs) were synthesized starting from 2‐hydroxyethylacrylate and fatty acids. The chemical structure and crystalline morphology of the synthesized copolymers were characterized with Fourier transform infrared and ¹H‐NMR spectroscopy and polarized optical microscopy, respectively, and their thermal energy storage properties and thermal stability were investigated with differential scanning calorimetry and thermogravimetric analysis, respectively. The thermal conductivities of the PCMs were also measured with a thermal property analyzer. Moreover, thermal cycling testing showed that the copolymers had good thermal reliability and chemical stability after they were subjected to 1000 heating/cooling cycles. The synthesized poly(2‐alkyloyloxyethylacrylate) polymers and poly(2‐alkyloyloxyethylacrylate‐co‐methylacrylate) copolymers as novel PCMs have considerable potential for thermal energy storage and temperature‐control applications. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Rapid design space exploration using legacy design data and technology scaling trend

Rapid and effective design space exploration at all stages of a design process enables faster design convergence and shorter time-to-market. This is particularly important during the early stage of a design where design decisions can have a significant impact on design convergence. This paper describes a methodology for design space exploration using design target prediction models. These models are driven by legacy design data, technology scaling trends and, an in situ model-fitting process. Experiments on ISCAS benchmark circuits validate the feasibility of the proposed approach and yielded power centric designs that improved power by 7-32% for a corresponding 0-9% performance impact; or performance centric designs with improved performance of 10.31-17% for a corresponding 2-3.85% power penalty. Evolutionary algorithm based Pareto analysis on an industrial 65 nm design uncovered design tradeoffs which are not obvious to designers and optimize both power and performance. The high performance design option of the industrial design improved the straight-ported design's performance by 29% with a 2.5% power penalty, whereas the low power design option reduced the straight-ported design's power consumption by 40% for a 9% performance penalty.