Surface enhanced Raman spectroscopy for microfluidic pillar arrayed separation chips

Numerous studies have addressed the challenges of implementing miniaturized microfluidic platforms for chemical and biological separation applications. However, the integration of real time detection schemes capable of providing valuable sample information under continuous, ultra low volume flow regimes has not fully been addressed. In this report we present a chip based chromatography system comprising of a pillar array separation column followed by a reagent channel for passive mixing of a silver colloidal solution into the eluent stream to enable surface enhanced Raman spectroscopy (SERS) detection. Our design is the first integrated chip based microfluidic device to combine pressure driven separation capability with real time SERS detection. With this approach we demonstrate the ability to collect distinctive SERS spectra with or without complete resolution of chromatographic bands. Computational fluidic dynamic (CFD) simulations are used to model the diffusive mixing behaviour and velocity profiles of the two confluent streams in the microfluidic channels. We evaluate the SERS spectral band intensity and chromatographic efficiency of model analytes with respect to kinetic factors as well as signal acquisition rates. Additionally, we discuss the use of a pluronic modified silver colloidal solution as a means of eliminating contamination generally caused by nanoparticle adhesion to channel surfaces.     

General and selective head-to-head dimerization of terminal alkynes proceeding via hydropalladation pathway

A general highly regio- and stereoselective palladium-catalyzed head-to-head dimerization reaction of terminal acetylenes is presented. This methodology allows for the efficient synthesis of a variety of 1,4-enynes as single E stereoisomers. Computational studies reveal that this dimerization reaction proceeds via the hydropalladation pathway.     

On the correlation between electromagnetic waves absorption and electrical conductivity of iron powder filled nitrile butadiene rubber

In the present paper the correlation between the coefficient of attenuation of electromagnetic microwaves and resistivity in iron powder filled (0‐500 phr) nitrile butadiene rubber based compositions has been investigated and evaluated by the coefficient of correlation. As a whole coefficient of correlation values between the electrical and microwave characteristics investigated are in the region (−0,99 ÷ −0,92), i.e. a reverse functional dependence, too close to the linear, is available, but a lightly expressed tendency to decreasing of correlation coefficients values when filler amount increases, has been observed. As a result above concentration of 300 phr the correlation is predominately stochastic.     

From simple structure to sparse components: a review

The article begins with a review of the main approaches for interpretation the results from principal component analysis (PCA) during the last 50–60 years. The simple structure approach is compared to the modern approach of sparse PCA where interpretable solutions are directly obtained. It is shown that their goals are identical but they differ by the way they are realized. Next, the most popular and influential methods for sparse PCA are briefly reviewed. In the remaining part of the paper, a new approach to define sparse PCA is introduced. Several alternative definitions are considered and illustrated on a well-known data set. Finally, it is demonstrated, how one of these possible versions of sparse PCA can be used as a sparse alternative to the classical rotation methods.     

Sparse principal components by semi-partition clustering

A cluster-based method for constructing sparse principal components is proposed. The method initially forms clusters of variables, using a new clustering approach called the semi-partition, in two steps. First, the variables are ordered sequentially according to a criterion involving the correlations between variables. Then, the ordered variables are split into two parts based on their generalized variance. The first group of variables becomes an output cluster, while the second one—input for another run of the sequential process. After the optimal clusters have been formed, sparse components are constructed from the singular value decomposition of the data matrices of each cluster. The method is applied to simple data sets with smaller number of variables (p) than observations (n), as well as large gene expression data sets with p ? n. The resulting cluster-based sparse principal components are very promising as evaluated by objective criteria. The method is also compared with other existing approaches and is found to perform well.     

On the size dependence of surface tension in the temperature range from melting point to critical point

The problem of size dependence of surface tension was investigated in view of a more general problem of the applicability of Gibbs’ thermodynamics to nanosized objects. For the first time, the effective surface tension (coinciding with the specific excess free energy for an equimolecular dividing surface) was calculated within a wide temperature range, from the melting temperature to the critical point, using the thermodynamic perturbation theory. Calculations were carried out for Lennard-Jones and metallic nanosized droplets. It was found that the effective surface tension decreases both, with temperature and particle size.     

Synthesis and Reactivity of New Organophosphorus Compounds

Abstract

Synthesis of functionally substituted organophosphorus compounds on the basis of reactions of the tri-n-butylphosphine/carbon disulfide, tri-n-butylphosphine/phenylisothiocyanate, tris(dimethylamino)phosphine/phenylisothiocyanate adducts with a wide range of different dipolarophiles are reported. The development of application of S-Li tri-n-butylphosphonio-dithioformiate derivatives to the synthesis of new types of organophosphorus compounds are reported.     

CSAR benchmark exercise of 2010: selection of the protein-ligand complexes

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose ( www.csardock.org ). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein-ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (K(d) or K(i)) in Binding MOAD, augmented with entries from PDB bind. The final data set contains 343 diverse protein-ligand complexes and spans 14 pK(d). Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; Pearson R is limited to ~ 0.91 (Pearson R2 0.83) when fitting to the data set without over parameterizing. Pearson R is limited to ~ 0.83(Pearson R2 ~ 0.70) when scoring the data set with a method trained on outside data [corrected]. The details of how the data set was initially selected, and the process by which it matured to better fit the needs of the community are presented. Many groups generously participated in improving the data set, and this underscores the value of a supportive, collaborative effort in moving our field forward.