In this paper, a technique based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using conventional techniques. The approach is based on formulating the parameter extraction as a search and optimization problem. Current-voltage data used were generated by simulating a two-diode solar cell model of specified parameters. The genetic algorithm search range that simulates the error in the extracted parameters was varied from ±5 to ±100% of the specified parameter values. Results obtained show that for a simulated error of ±5% in the solar cell model values, the deviation of the extracted parameters varied from 0.1 to 1% of the specified values. Even with a simulated error of as high as ±100%, the resulting deviation only varied from 2 to 36%. The performance of this technique is also shown to surpass the quasi-Newton method, a calculus-based search and optimization algorithm.

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Electrodes composed of activated carbon cloth (ACC) coated with zinc oxide (ZnO) nanorods are compared with plain ACC electrodes, with respect to their desalination efficiency of a 17 mM NaCl solution at different applied potentials. Polarization of the ZnO nanorods increased the penetration depth and strength of the electric field between the electrodes, leading to an increase in the capacitance and charge efficiency at reduced input charge ratios. Uniform distribution of the electric field lines between two electrodes coated with ZnO nanorods led to faster ion adsorption rates, reduced the electrode saturation time, and increased the average desalination efficiency by ∼45% for all applied potentials. The electrodes were characterized for active surface area, capacitance from cyclic voltammetry, theoretical assessment of surface area utilization, and the magnitude of electric field force acting on an ion of unit charge for each potential.

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