Geometries of the Halocarbene anions HCF- and CF2-: ab initio calculation and Franck-Condon analysis

A theoretical method to calculate multidimensional Franck-Condon factors including Duschinsky effects is described and used to simulate the photoelectron spectra of HCF- and CF2- radicals. Geometry optimization and harmonic vibrational frequency calculations have been performed on the (X) over tilde (1)A' state of HCF and (X) over tilde (2)A" state of HCF-, and (X) over tilde (1)A(1) state of CF2 and (X) over tilde B-2(1) state of CF2-. Franck-Condon analyses and spectral simulation were carried out on the first photoelectron band of HCF- and CF2- respectively. The theoretical spectra obtained by employing B3LYP/6-311 + G(2d,p) values are in excellent agreement with the observed ones. In addition, the equilibrium geometry parameters, R(CF) = 0.1475 +/- 0.0005 nm, of the (X) over tilde (2)A" state of HCF-, and r(FC) = 0.1425 +/- 0.0005 nm and angle(FCF) = 100.5 +/- 0.5degrees, of the (X) over tilde B-2(i) state of CF2-, are derived by employing an iterative Franck-Condon analysis procedure in the spectral simulation. (C) 2003 Elsevier B.V. All rights reserved.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Modeling and optimization for separation of ionic solutes in pressurized flow capillary electrochromatography

By manipulation of applied pressure or voltage, pressurized flow capillary electrochromatography (P-CEC) permits unique control of selectivity for ionic solutes. A simple mathematical model has been developed to describe the quantitative relationship between the electrochromatographic retention factor (k(*)) of charged solutes and the applied voltage and pressure. The validity of the model was verified experimentally with hydrophilic interaction mode CEC (HI-CEC). On the basis of the model developed, it was found that the value of k(*) could be predicted accurately using only a limited number of data points from the initial experiments at different voltages or pressures. Correlation between the experimentally measured and calculated k(*) was excellent, with a correlation coefficient greater than 0.999. Optimization for the separation of peptides by P-CEC was also performed successfully on the basis of the proposed model.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

solving global unconstrained optimization problems by symmetry-breaking

IEEE Computer Society; International Association for; Computer and Information Science, ACIS

A spatial sampling optimization package using MSN theory

The density and distribution of spatial samples heavily affect the precision and reliability of estimated population attributes. An optimization method based on Mean of Surface with Nonhomogeneity (MSN) theory has been developed into a computer package with the purpose of improving accuracy in the global estimation of some spatial properties, given a spatial sample distributed over a heterogeneous surface; and in return, for a given variance of estimation, the program can export both the optimal number of sample units needed and their appropriate distribution within a specified research area. (C) 2010 Elsevier Ltd. All rights reserved.

Red-Light-Emitting Iridium Complexes with Hole-Transporting 9-Arylcarbazole Moieties for Electrophosphorescence Efficiency/ Color Purity Trade-off Optimization

The synthesis, structures, photophysics, electrochemistry and electrophosphorescent properties of new red phosphorescent cyclometalated iridium(III) isoquinoline complexes, bearing 9-arylcarbazolyl chromophores, are reported. The functional properties of these red phosphors correlate well with the results of density functional theory calculations. The highest occupied molecular orbital levels of these complexes are raised by the integration of a carbazole unit to the iridium isoquinoline core so that the hole-transporting ability is improved in the resulting complexes relative to those with I-phenylisoquinoline ligands. All of the complexes are highly thermally stable and emit an intense red light at room temperature with relatively short lifetimes that are beneficial for highly efficient organic light-emitting diodes (OLEDs).

Characterization and optimization of AuNPs labeled by Raman reporters on glass based on silver enhancement

In this report, gold nanoparticles (AuNPs) labeled by Raman reporters (AuNPs-R6G) were assembled on glass and used as the seeds to in situ grow silver-coated nanostructures based on silver enhancer solution, forming the nanostructures of AuNPs-R6G@Ag, which were characterized by scanning electron microscopy (SEM) and UV-visible spectroscopy. More importantly, the obtained silver-coated nanostructures can be used as a surface enhancement Raman scattering (SERS) substrate. The different SERS activities can be controlled by the silver deposition time and assembly time of AuNPs-R6G on glass. The results indicate that the maximum SERS activity could be obtained on AuNPs-R6G when these nanostructures were assembled on glass for 2 h with silver deposition for 2 min.

Design of silicone rubber according to requirements based on the multi-objective optimization of chemical reactions

The explicit expression between composition and mechanical properties of silicone rubber was derived from the physics of polymer elasticity, the implicit expression among material composition, reaction conditions and reaction efficiency was obtained from chemical thermodynamics and kinetics, and then an implicit multi-objective optimization model was constructed. Genetic algorithm was applied to optimize material composition and reaction conditions, and the finite element method of cross-linking reaction processes was used to solve multi-objective functions, on the basis of which a new optimization methodology of crosslinking reaction processes was established. Using this methodology, rubber materials can be designed according to pre-specified requirements.

A new solid-supported iterative divergent/convergent strategy for the synthesis of dendrimers

A rapid and convenient solid-supported iterative divergent/converpent approach was developed to prepare rigid phenylacetylene dendrimers. The generation number grows very rapidly and the purification at each step is very simple. (C) 2001 Elsevier Science Ltd. All rights reserved.

Factor analysis used to peak position optimization and spectral interference correction in inductively coupled plasma atomic emission spectrometry

Target transformation factor analysis was used to correct spectral interference in inductively coupled plasma atomic emission spectrometry (ICP-BES) for the determination of rare earth impurities in high purity thulium oxide. Data matrix was constructed with pure and mixture vectors and background vector. A method based on an error evaluation function was proposed to optimize the peak position, so the influence of the peak position shift in spectral scans on the determination was eliminated or reduced. Satisfactory results were obtained using factor analysis and the proposed peak position optimization method.