Combinatorial synthesis and characterization of a ternary epitaxial film of Co and Mn doped Ge (0 0 1)

We report combinatorial molecular beam epitaxy synthesis and properties of a ternary epitaxial film of Co and Mn co-doped Ge grown on Ge (0 0 1) substrate. Structural effects were examined in situ by reflection high-energy electron diffraction and ex situ by microbeam X-ray diffraction techniques, and magnetic properties were probed by using magnetooptic Kerr effect. Ternary epitaxial phase diagrams have been studied for total doping concentrations up to 30 at.%, where regions of coherent epitaxy and rough disordered growth and those of near room temperature ferromagnetic ordering have been identified.     

Accurate and precise spectrochemical analysis of Bi-Pb-Sr-Ca-Cu-O high-temperature superconductor materials

For quality tests of ceramic Bi-Pb-Sr-Ca-Cu-O superconductor materials (precursors and final products) accurate stoichiometric determinations of the metallic major components are necessary. Three methods were developed during the last decade in our group. Their properties and the results obtained are compared. The first classical analytical procedure requires to much manpower. The second combined chemical-spectrometric procedure is a routine method today, but also unsuitable for rapid tests. A new fully spectrometric procedure was recently developed using a simultaneously working Echelle spectrometer with CID detector, autosampler and a special self-made data evaluation software. The basis of this method are multi-line measurements of each analyte element, a new method of spectral line selection for main component precision determinations, multi-component calibrations, and frequent external standardizations. For this method the sum of the confidence intervals of all element determinations was less than 1%, and no systematic error was detected.     

Parallelizing Nonlinear Least-Squares Regression with Application to Analyses of Microalgae

Nonlinear least-squares regression is a valuable tool for gaining chemical insights into complex systems. Yet, the success of nonlinear regression as measured by residual sum of squares (RSS), correlation, and reproducibility of fit parameters strongly depends on the availability of a good initial solution. Without such, iterative algorithms quickly become trapped in an unfavorable local RSS-minimum. For determining an initial solution, a high-dimensional parameter space needs to be screened, a process that is very time-consuming but can be parallelized. Another advantage of parallelization is equally important: After determining initial solutions, the used processors can be tasked to each optimize an initial guess. Even if several of these optimizations become stuck in a shallow local RSS-minimum, other processors continue and improve the regression outcome. A software package for parallel processing-based constrained nonlinear regression (RegressionLab) has been developed, implemented, and tested on a variety of hardware configurations. As proof-of-principle, microalgae to environment interactions have been studied by infrared attenuated total reflection spectroscopy. Additionally, light microscopy has been used to monitor cell production. It is shown that spectroscopic data sets with 10,000?s of data points and >1000 nonlinear model parameters as well as imaging data with 100,000s of data points and >2000 nonlinear model parameters may now be investigated by constrained nonlinear regression. Acceleration factors of up to 8.1 have been obtained which is of high practical relevance when computations take weeks on single-processor machines. Solely using parallel processing, the RSS values may be improved up to a factor of 5.5.     

Recent developments in the chemistry of donor-functionalized phosphinines

This review provides an overview of the most significant strategies developed in recent years for the preparation of donor-functionalized phosphinines and new developments in this field are highlighted. Selected synthetic procedures for the introduction of additional sulfur-, oxygen-, phosphorus-, and nitrogen-donor groups into the heterocyclic framework are presented. Examples of their coordination chemistry and potential applications in homogeneous catalysis and phosphorus containing molecular materials will be given.     

TiO2 thick films supported on reticulated macroporous Al2O3 foams and their photoactivity in phenol mineralization

TiO₂ thick films deposited on macroporous reticulated Al₂O₃ foams with pore size of 10 ppi and 15 ppi were prepared using dip coating from slurries of Aeroxide^® P25 nanopowder and precipitated titania. All prepared films have sufficiently good adhesion to the surface of the substrate also in case of strongly cracked films. No measurable release of deposited TiO₂ after repeated photocatalytic cycles was observed. The photocatalytic activity was characterized as the rate of mineralization of aqueous phenol solution under irradiation of UVA light by TOC technique. The best activity was obtained with Aeroxide^® P25 coated Al₂O₃ foam with the pore size of 10 ppi, annealed at 600 °C. The optimal annealing temperature for preparation of films from precipitated titania could be determined at 700 °C. Films prepared by sol-gel deposition technique were considerably thinner compared to coatings made of suspensions and their photocatalytic activity was significantly smaller.     

Introducing nonlinear,multivariate ‘Predictor Surfaces’ for quantitative modeling of chemical systems with higher-order,coupled predictor variables

Innovations in chemometrics are required for studies of chemical systems which are governed by nonlinear responses to chemical parameters and/or interdependencies (coupling) among these parameters. Conventional and linear multivariate models have limited use for quantitative and qualitative investigations of such systems because they are based on the assumption that the measured data are simple superpositions of several input parameters. ‘Predictor Surfaces’ were developed for studies of more chemically complex systems such as biological materials in order to ensure accurate quantitative analyses and proper chemical modeling for in-depth studies of such systems. Predictor Surfaces are based on approximating nonlinear multivariate model functions by multivariate Taylor expansions which inherently introduce the required coupled and higher-order predictor variables.