A bis-selenophene substituted alkoxy benzene derivative as a highly stable novel electrochromic polymer

Up to date there are few studies reporting the use of selenophene derivatives as electrochromic polymers. This report highlights the synthesis of a selenophene containing multi-ring aromatic polymer which possesses reasonable optical contrasts at both visible and near-IR regions. Electrochemical synthesis of a conducting polymer from a multi-ring aromatic monomer, 2-(2,5-bis(hexyloxy)-4-(selenophen-2-yl)phenyl)selenophene (BSB(OC₆H₁₃)₂), was achieved at a lower potential than its corresponding parent, selenophene. The strong absorption band for the undoped polymer was 474 nm and the onset energy for the π–π^* transition (Eg) was 1.9 eV (645 nm).A novel dual-polymer electrochromic device was constructed in sandwich configuration where poly(BSB(OC₆H₁₃)₂) was used as the anodically coloring material. It is the first electrochromic device where a selenophene-containing polymer was used as an active layer. The device switches between a rose-colored state and a blue-colored state with a fast response time (less than 1 s).     

Water-soluble poly(4-styrenesulfonic acid-co-maleic acid) stabilized ruthenium(0) and palladium(0) nanoclusters as highly active catalysts in hydrogen generation from the hydrolysis of ammonia–borane

Water-soluble poly(4-styrenesulfonic acid-co-maleic acid), PSSA-co-MA, stabilized ruthenium(0) and palladium(0) nanoclusters were for the first time prepared in situ from the reduction of ruthenium(III) chloride and potassium tetrachloropalladate(II), respectively, by ammonia–borane during its hydrolysis at room temperature. PSSA-co-MA stabilized ruthenium(0) and palladium(0) nanoclusters having average particle size of 1.9 ± 0.5 and 3.5 ± 1.6 nm, respectively, were isolated from the reaction solution and characterized by TEM and UV–visible electronic absorption spectroscopy. PSSA-co-MA stabilized ruthenium(0) and palladium(0) nanoclusters are highly active catalysts for hydrogen generation from the hydrolysis of ammonia–borane at low temperature. PSSA-co-MA stabilized ruthenium(0) and palladium(0) nanoclusters provide 51,720 and 8720 turnovers, respectively, in the hydrogen generation from the hydrolysis of ammonia–borane at 25 °C before deactivation. Catalytic hydrolysis of ammonia–borane is first order with respect to the catalyst concentration, but zero order with respect to the substrate concentration in the case of both ruthenium(0) and palladium(0) nanoclusters. Activation energies for the hydrolysis of ammonia–borane in the presence of PSSA-co-MA stabilized ruthenium(0) or palladium(0) nanoclusters (54 ± 2 kJ mol⁻¹ and 44 ± 2 kJ mol⁻¹, respectively) are smaller than most of the values reported for the same reaction in the presence of other catalyst systems.     

Effect of injection timing on the exhaust emissions of a diesel engine using diesel–methanol blends

Environmental concerns and limited resource of petroleum fuels have caused interests in the development of alternative fuels for internal combustion (IC) engines. For diesel engines, alcohols are receiving increasing attention because they are oxygenated and renewable fuels. Therefore, in this study, the effect of injection timing on the exhaust emissions of a single cylinder, naturally aspirated, four-stroke, direct injection diesel engine has been experimentally investigated by using methanol-blended diesel fuel from 0% to 15% with an increment of 5%. The tests were conducted for three different injection timings (15°, 20° and 25 °CA BTDC) at four different engine loads (5 Nm, 10 Nm, 15 Nm, 20 Nm) at 2200 rpm. The experimental test results showed that Bsfc, NO_x and CO₂ emissions increased as BTE, smoke opacity, CO and UHC emissions decreased with increasing amount of methanol in the fuel mixture. When compared the results to those of original injection timing, NO_x and CO₂ emissions decreased, smoke opacity, UHC and CO emissions increased for the retarded injection timing (15 °CA BTDC). On the other hand, with the advanced injection timing (25 °CA BTDC), decreasing smoke opacity, UHC and CO emissions diminished, and NO_x and CO₂ emissions boosted at all test conditions. In terms of Bsfc and BTE, retarded and advanced injection timings gave negative results for all fuel blends in all engine loads.     

Optimization of Multireservoir Systems by Genetic Algorithm

Application of optimization techniques for determining the optimal operating policy of reservoirs is a major issue in water resources planning and management. As an optimization Genetic Algorithm, ruled by evolution techniques, have become popular in diversified fields of science. The main aim of this study is to explore the efficiency and effectiveness of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results obtained were compared to the real operational data and genetic algorithm was found to be effective and can be utilized as an alternative technique to other traditional optimization techniques.     

Optimal tuning of PI speed controller coefficients for electric drives using neural network and genetic algorithms

This paper presents a method of tuning Proportional Integral (PI) controller coefficients in the off-line control of a nonlinear system. In this method, the first step is the identification of the system via Artificial Neural Networks (ANNs), using maximum overshoot and settling time obtained from the application circuit for different K_p-K_i pairs. With this in mind, multi-layer ANN, which uses back-propagation of the error algorithm, was used as the learning algorithm. In the second step, the purpose is to find the optimum controller coefficients using the ANN model as the objective function via Genetic Algorithms (GAs). A Digital Signal Processor (DSP-TMS320C50) was used to carry out control applications. The C++ language was used for ANN and GA, and and the Assembly language was used for the DSP. It is determined that maximum overshoot and settling time are very small if the system is controlled by control parameters obtained from the optimization process that uses GA.