Energy Harvesting System Design and Optimization Using High Bandwidth Rectenna for Wireless Sensor Networks

Wireless Personal Communications - Today’s world has a keen interest in systems which are battery-less and can harvest energy from their surroundings. There is an increased demand for...     

Development of LTCC micro-hotplate with PTC temperature sensor for gas-sensing applications

Low temperature co-fired ceramic (LTCC) micro-hotplates show wide applications in gas sensors and micro-fluidic devices. It is easily structured in three-dimensional structures. This paper presents the low power consumption micro-hotplates which were developed with PTC (positive temperature coefficient) temperature sensor and inter-digitated electrodes. The paper presents two different structures for micro-hotplate with platinum as a heating element. The PTC temperature sensor using two different materials viz. PdAg and platinum paste are developed with micro-hotplates. The simulation has been achieved through COMSOL for LTCC and alumina micro-hotplates. The temperature variation with power consumption has been measured for the developed LTCC micro-hotplates. The change in resistance of PTC temperature sensors was measured with micro-hotplate temperature. The aim of this study was to place a temperature sensor with the gas sensor module to measure and control the temperature of micro-hotplate. A SnO₂ sensing layer is coated on LTCC micro-hotplate using screen printing and characterized for the sensing of carbon monoxide gas (CO). This study will be beneficial for designing hotplates based on LTCC technology with low power consumption and better stability of temperature for gas-sensing applications.     

Synthesis and characterization of poly(ethylene aspartate)-metal complexes

Poly(ethylene aspartate) [PEA] was synthesized by the melt condensation of D,L-aspartic acid and ethylene glycol. PEA containing pendent amino and carbonyl groups in its repeating chain was used as the polymeric ligand for complexation with transition metal ions, viz. Co(II), Ni(II), Cu(II), Mn(II), Zn(II), Cd(II), Ca(II), Mg(II), Pb(II) and Hg(II). Complexation was found to be most effective in DMSO. The resulting polyester-metal complexes were solid coloured materials which have been characterized by IR spectroscopy, elemental analysis and magnetic susceptibility measurements. The thermal stability of the polyester-metal complexes was investigated by thermogravimetric analysis (TGA). On the basis of the physico-chemical studies, an oxygen and nitrogen coordinated structure for the polyester-metal complexes is proposed.     

Melt intercalation/exfoliation of polystyrene-sodium-montmorillonite nanocomposites using sulfonated polystyrene ionomer compatibilizers

Quaternary ammonium salts of sulfonated polystyrene (SPS) were used as compatibilizers for melt intercalation of PS and pristine Na-montmorillonite. Tetra-octyl ammonium SPS and tetra-decyl ammonium SPS ionomeric compatibilizers produced significant exfoliation and a homogeneous dispersion of the polymer-clay nanocomposites. Wide angle X-ray diffraction and transmission electron microscopy were primarily used to characterize the morphology of the nanocomposites. Image analysis was used to measure the percentage exfoliation. Exfoliation increased with the increasing length of the alkyl chain of the ammonium counter-ion of the SPS ionomer. The nanocomposites containing ionomers exhibited higher storage moduli compared to nanocomposites without the compatibilizer.     

Countercurrent water-heating system with a heat exchanger in a solar energy collector loop

The counter current heat exchanger theory with periodic inlet temperatures has been applied to analyze the thermal performance of a water heating system. The theoretical results obtained are in agreement with the experimental data reported in the literature.     

A scheduling decision aid in glass fiber manufacturing

A decision aid for scheduling production in glass fiber manufacturing industry is described. The methodology combines a linear programming (LP) optimization model with a heuristic model. The LP model determines production goals; the heuristic model then uses the LP output to incorporate system-specific constraints in developing processing sequences.