A New Approach to Estimation of the Electrocrystallization Parameters

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@Article{JingxianYu:1999:EAC,

• author = "Jingxian Yu and Hongqing Cao and Yongyan Chen and Lishan Kang and Hanxi Yang",
• title = "A New Approach to Estimation of the Electrocrystallization Parameters",
• journal = "Journal of Electroanalytical Chemistry",
• year = "1999",
• volume = "474",
• number = "1",
• pages = "69--73",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Electrocrystallization parameters, Zinc, Parameters estimation",
• ISSN = "1572-6657",
• URL = "http://www.sciencedirect.com/science/article/pii/S0022072899003071",
• DOI = "doi:10.1016/S0022-0728(99)00307-1",
• size = "5 pages",
• abstract = "To overcome the drawbacks in estimating electrocrystallization parameters using traditional methods, we propose a genetic algorithm using a novel crossover operator based on the non-convex linear combination of multiple parents to estimate the electrocrystallisation parameters A (the nucleation rate constant), N0 (the nucleation density) and D (the diffusion coefficient of Zn2+ ions) simultaneously in the general current-time expression of Scharifker and Mostany for nucleation and growth by fitting the whole current transients for zinc electrodeposition onto glassy carbon electrode immersed in the acetate solutions. By running the algorithm, we obtained for different step potentials, D values close to 2.10cm2/sec/1000000, which are comparable to reported values. The values of A obtained for all step potentials are identical, 1.41/sec/1000000000, which indicates that zinc deposition onto glassy carbon electrode follows three-dimensional instantaneous nucleation and growth. In addition, from the values of N0 obtained, one can observe that an increase in step potential leads to a higher N0. These results show that our algorithm works stably and effectively in solving the problem of estimating the electrocrystallisation parameters, and more importantly, it can be extended easily to a general algorithm to estimate multiple parameters in an arbitrary chemical model.",

}

Genetic Programming entries for Jingxian Yu Hong-Qing Cao Yongyan Chen Li-Shan Kang Hanxi Yang

Citations