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2 2009

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Hydrothermal synthesis of aqueous nano-TiO2 sols

Mahata S., Kundu D.

Materials Science Poland

2009

Vol. 27, No. 2

465--470

A poorly crystalline nano-anatase precursor with the crystallite sizes of about 3.5 nm was prepared via reprecipitation of nano-anatase at pH = 7.0 from a highly transparent sol obtained by acidic peptization of hydrous titania precipitates, hydrolysed product of titanium isopropoxide. The weakly agglomerated nano-anatase precursor has been used for preparing well crystalline nano-TiO2 sols by hydrothermal treatment at pH = 9.5 using a weak base, NH4OH. The crystallite size gradually grew with increasing temperature and reached 7.6 nm (X-ray) at 175 °C after 4 h of hydrothermal treatment. The sizes of particles were mostly in the range 5-15 nm. The hydroxyl content in the nano-TiO2 decreased with increasing hydrothermal temperature of treatment and supported a gradual growth of nano-TiO2 as evident from the X-ray and TEM.

Data Clustering Using Multi-objective Differential Evolution Algorithms

Suresh K., Kundu D., Ghosh S., Das S.

Fundamenta Informaticae

2009

Vol. 97, nr 4

381-403

The article considers the task of fuzzy clustering in a multi-objective optimization (MO) framework. It compares the relative performance of four recently developedmulti-objective variants of Differential Evolution (DE) on over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation for the candidates is used for DE. A comparative study of four DE variants with two most well-known MO clustering techniques, namely the NSGA II (Non Dominated Sorting GA) and MOCK (Multi-Objective Clustering with an unknown number of clusters K) is also undertaken. Experimental results reported for six artificial and four real life datasets (including a microarray dataset of budding yeast) of varying range of complexities indicates that DE can serve as a promising algorithm for devising MO clustering techniques.