Energy levels, wavefunction compositions and electric dipole transitions in neutral Ca

A configuration-interaction approach, based on the use of B-spline basis sets combined with a model potential including monoelectronic and dielectronic core polarization effects, is employed to calculate term energies and wavefunctions for neutral Ca. Results are reported for singlet and triplet bound states, and some quasi-bound states above the lowest ionization limit, with angular momentum up to L = 4. Comparison with experiment and with other theoretical results shows that this method yields the most accurate energy values for neutral Ca obtained to date. Wavefunction compositions, necessary for labelling the levels, and the effects of semi-empirical polarization potentials on the wavefunctions are discussed, as are some recent identifications of doubly-excited states. It is shown that taking into account dielectronic core polarization changes the energies of the lowest terms in Ca significantly, in general by a few hundred cm(-1), the effect decreasing rapidly for the higher bound states. For Rydberg states with n approximate to 7 the accuracy of the results is often better than a few cm(-1). For series members (or perturbers) with a pronounced 3d character the error can reach 150 cm(-1). The wavefunctions are used to calculate oscillator strengths and lifetimes for a number of terms and these are compared with existing measurements. The agreement is good but points to a need for improved measurements.

Drug Polymer Thermodynamic Phase Diagrams in the Selection of Optimal API-Polymer Compositions for Amorphous Solid Dispersions

Purpose The aim of this work was to examine, for amorphous solid dispersions, how the thermal analysis method selected impacts on the construction of thermodynamic phase diagrams, and to assess the predictive value of such phase diagrams in the selection of optimal, physically stable API-polymer compositions. Methods Thermodynamic phase diagrams for two API/polymer systems (naproxen/HPMC AS LF and naproxen/Kollidon 17 PF) were constructed from data collected using two different thermal analysis methods. The “dynamic” method involved heating the physical mixture at a rate of 1 &[deg]C/minute. In the "static" approach, samples were held at a temperature above the polymer Tg for prolonged periods, prior to scanning at 10 &[deg]C/minute. Subsequent to construction of phase diagrams, solid dispersions consisting of API-polymer compositions representative of different zones in the phase diagrams were spray dried and characterised using DSC, pXRD, TGA, FTIR, DVS and SEM. The stability of these systems was investigated under the following conditions: 25 &[deg]C, desiccated; 25 &[deg]C, 60 % RH; 40 &[deg]C, desiccated; 40 &[deg]C, 60 % RH. Results Endset depression occurred with increasing polymer volume fraction (Figure 1a). In conjunction with this data, Flory-Huggins and Gordon-Taylor theory were applied to construct thermodynamic phase diagrams (Figure 1b). The Flory-Huggins interaction parameter (&[chi]) for naproxen and HPMC AS LF was + 0.80 and + 0.72, for the dynamic and static methods respectively. For naproxen and Kollidon 17 PF, the dynamic data resulted in an interaction parameter of - 1.1 and the isothermal data produced a value of - 2.2. For both systems, the API appeared to be less soluble in the polymer when the dynamic approach was used. Stability studies of spray dried solid dispersions could be used as a means of validating the thermodynamic phase diagrams. Conclusion The thermal analysis method used to collate data has a deterministic effect on the phase diagram produced. This effect should be considered when constructing thermodynamic phase diagrams, as they can be a useful tool in predicting the stability of amorphous solid dispersions.

Fragrance compositions comprising ionic liquids

The present invention relates to a fragrance composition comprising ionic liquids for enhanced evaporation of the perfume raw materials. The invention also relates to methods of use of the fragrance compositions for perfuming suitable substrates, particularly skin and hair.

Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al 2 O 3 /SiO 2, Na 2 O/Al 2 O 3, Na 2 O/H 2 O, and Na/[Na+K], more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated alumina-silica natural products using alkaline hydroxide, and alkaline silicate. The research results are in agreement with mechanism of geopolymerization.


Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)MT.1943-5533.0000829