Experimental and theoretical investigation of the structural, chemical, electronic, and high frequency dielectric properties of barium cadmium tantalate-based ceramics

Single-phase Ba(Cd1/3Ta2/3)O-3 powder was produced using conventional solid state reaction methods. Ba(Cd1/3Ta2/3)O-3 ceramics with 2 wt % ZnO as sintering additive sintered at 1550 degreesC exhibited a dielectric constant of similar to32 and loss tangent of 5x10(-5) at 2 GHz. X-ray diffraction and thermogravimetric measurements were used to characterize the structural and thermodynamic properties of the material. Ab initio electronic structure calculations were used to give insight into the unusual properties of Ba(Cd1/3Ta2/3)O-3, as well as a similar and more widely used material Ba(Zn1/3Ta2/3)O-3. While both compounds have a hexagonal Bravais lattice, the P321 space group of Ba(Cd1/3Ta2/3)O-3 is reduced from P (3) under bar m1 of Ba(Zn1/3Ta2/3)O-3 as a result of a distortion of oxygen away from the symmetric position between the Ta and Cd ions. Both of the compounds have a conduction band minimum and valence band maximum composed of mostly weakly itinerant Ta 5d and Zn 3d/Cd 4d levels, respectively. The covalent nature of the directional d-electron bonding in these high-Z oxides plays an important role in producing a more rigid lattice with higher melting points and enhanced phonon energies, and is suggested to play an important role in producing materials with a high dielectric constant and low microwave loss. (C) 2005 American Institute of Physics.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.