The crystal and molecular structure of bis (pyridoxamine) copper (II) dinitrate monohydrate

The crystal structure of Cu(PM)2(N03hoH20 (where PM is pyridoxamine, CSHI2N202) has been determined from three dimensional x-ray diffraction data. The crystals are triclinic, space group pI, a = 14.248 (2), b = 8.568 (1), c = 9.319 (1) 1, a = 94.08 (1), e = 89.73 (1), y~~ 99.18 (1)°, z = 2, jl(MoK) = 10.90 em-I, Po = 1.61 g/cm3 and Pc = 1.61 g/em3• The structure a was solved by Patterson techniques from data collected on a Picker 4-circle diffractometer to 26max = 45°. All atoms, including hydrogens, have been located. Anisotropic thermal parameters have been refined for all nonhydrogen atoms. For the 2390 independent reflections with F ? 3cr(F) , R = 0.0408. The results presented here provide the first detailed structural information of a metal complex with PM itself. The copper atoms are located on centres of symmetry and each is chela ted by two PM zwitterions through the amino groups and phenolate oxygen atoms. The zwitterionic form found in this structure involves the loss of a proton from the phenolate group and protonation of the pyridine ring nitrogen atoms. The two independent Cu(PM)2 moieties are symmetrically bridged by a single oxygen atom from one of the nitrate groups. The second nitrate group is not coordinated to the copper atoms but is central to an extensive hydrogen bonding network involving the water molecule and uncoordinated functional groups of PM.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.