Interaction of cationic water-soluble meso-tetrakis(4-N-methylpyridiniumyl)porphyrin (TMPyP) with ionic and nonionic micelles: aggregation and binding

The equilibrium of meso-tetrakis(4-N-methylpyridiniumyl)porphyrin (TMPyP) in aqueous solution in the presence of surfactants was studied by optical spectroscopic techniques and SAXS (small angle X-ray scattering). Anionic SDS (sodium dodecyl sulfate), zwitterionic HPS (N-hexadecyl-N,N-dimethyl-3-ammonio-1-propanesulfonate) and nonionic TRITON X-100 (t-octyl-phenoxypolyethoxyethanol), surfactants were used. TMPyP is characterized by a protonation equilibrium with a pK(a) around 1.0, associated with the diacid-free base transition, and a second pK(a) around 12.0 related with the transition between the free base and the monoanion form. Three independent species were observed for TMPyP at pH 6.0 as a function of SDS concentration: free TMPyP, TMPyP-SDS aggregates and porphyrin monomer bound to micelles. For HPS and TRITON X-100, the equilibrium of TMPyP as a function of pH is quite similar to that obtained in pure aqueous solution: no aggregation was observed, suggesting that electrostatic contribution is the major factor in the interaction between TMPyP and surfactants. SAXS data analysis demonstrated a prolate ellipsoidal shape for SDS micelles; no significant changes in shape and size were observed for SDS-TMPyP co-micelles. Moreover, the ionization coefficient, alpha, decreases with the increase of the porphyrin concentration, suggesting the ""screening"" of the anionic charge of SDS by the cationic porphyrin. These results are consistent with optical absorption, fluorescence and RLS (resonance light scattering) spectroscopies data, allowing to conclude that neutral surfactants present a smaller interaction with the cationic porphyrin as compared with an ionic surfactant. Therefore, the interaction of TMPyP with the ionic and nonionic surfactants is predominantly due to the electrostatic contribution. Copyright (c) 2008 Society of Porphyrins & Phthalocyanines.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.