Cyclic voltammetry modeling, geometries, and electronic properties for metallofullerene complexes with μ3-η2:η2:η2-C60 bonding mode.
ABSTRACT Reduction potential (E(red)) values have been calculated and compared with available cyclic voltammetry (CV) data for 10 metallofullerene complexes with the mu(3)-eta(2):eta(2):eta(2)-C(60) (M(3)-C(6)[C(60)]) bonding mode. Consideration of bulk solvent effects is essential for the calculation of the E(red) values. Scaling factors for the electrostatic terms of the solvation energies have been introduced to fully describe the experimental cyclic voltammograms with a small mean deviation of 0.07 V. Multiple electron reductions induce movement of the metal cluster moieties on the C(60) surface, which is accompanied with the changes in some M-C[C(60)] bonds from pi-type to sigma-type mode. However, the changes in M(3)-C(60) distances, as well as the geometric changes of M(3) and C(60), are small for the reductions, which is in harmony with the high chemical and electrochemical stability of the metallofullerenes. Our population analyses reveal that the added electrons are not localized at the C(60) moieties, and electron population in the metal clusters is significant, more than 20% (av. 37%), for all the reductions. Furthermore, we demonstrated that the two close one-electron redox waves in CV diagrams are strongly correlated with significant electron delocalization, about 40-80%, to the metal-cluster moieties in these metallofullerene complexes.
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ABSTRACT: Current practice of validating predicted protein structural model is knowledge-based where scoring parameters are derived from already known structures to obtain decision on validation out of this structure information. For example, the scoring parameter, Ramachandran Score gives percentage conformity with steric-property higher value of which implies higher acceptability. On the other hand, Force-Field Energy Score gives conformity with energy-wise stability higher value of which implies lower acceptability. Naturally, setting these two scoring parameters as target objectives sometimes yields a set of multiple models for the same protein for which acceptance based on a particular parameter, say, Ramachandran score, may not satisfy well with the acceptance of the same model based on other parameter, say, energy score. The confusion set of such models can further be resolved by introducing some parameters value of which are easily obtainable through experiment on the same protein. In this piece of work it was found that the confusion regarding final acceptance of a model out of multiple models of the same protein can be removed using a parameter Surface Rough Index which can be obtained through semi-empirical method from the ordinary microscopic image of heat denatured protein.Bioinformation 01/2012; 8(20):984-7. · 0.50 Impact Factor