C(60), vitamin E, and three C(60) derivatives (polar 1 and water-soluble C(3)/D(3)C(60)s) were examined for their antioxidant effects on prevention of lipid peroxidation induced by superoxide and hydroxyl radicals. The protection effect on lipid peroxidation was found to be in the sequence: C(60) >/= vitamin E > 1 > none, for liposoluble antioxidants, and C(3)C(60) >> D(3)C(60) > none, for water-soluble ones. Fluorescence quenching of PyCH(2)COOH (Py = pyrene) by both C(3)- and D(3)C(60)s shows that the Stern-Volmer constant, K(SV), is about the same for both quenchers in aqueous solution. Upon addition of liposomes, the fluorescence quenching becomes more efficient: 5-fold higher in K(SV) for C(3)C(60) than for D(3)C(60). When Py(CH(2))(n)()COOH (n = 1, 3, 5, 9, or 15) was incorporated in lipid membranes, the K(SV)s all were small and nearly equal for D(3)C(60) but were quite large and different for C(3)C(60) with the sequence: n = 1 < 3 < 5 < 9 < 15. The better protection effect of C(3)C(60) on lipid peroxidation than that of D(3)C(60) is attributed to its stronger interaction with membranes. Overall, the antioxidation abilities of the compounds examined were rationalized in terms of the number of reactive sites, the location of antioxidant in lipid membranes, and the strength of interactions between antioxidants and membranes.

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In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.