Behavior of acetonitrile confined to mesoporous silica gels as studied by 129Xe NMR: a novel method for determining the pore sizes

129Xe NMR spectra of xenon dissolved in acetonitrile confined into three mesoporous silica gels with nominal pore diameters of 40, 60, and 100 A have been measured over the temperature range 170-245 K. The spectra consist of a number of lines, which contain detailed information on the system. The most interesting result is that the chemical shift of a particular signal observed below the melting point of confined acetonitrile is highly sensitive to the pore size, and hence its shape is sensitive to the pore size distribution function. This signal originates from the xenon atoms sited in very small cavities built up inside the pores during the freezing transition. It can be used to determine the size or even the size distribution function of the pores. In addition, the emergence of this signal reveals the phase transition temperature of acetonitrile inside the pores, which can also be used to determine the size of the pores. The difference in the chemical shifts of two other signals, which arise from xenon dissolved in bulk and confined acetonitrile, provides still another novel method for determining the size of the pores.     

Relativistic spin-orbit coupling effects on secondary isotope shifts of (13)C nuclear shielding in CX(2) (X = O, S, Se, Te)

Rovibrational corrections, temperature dependence, and secondary isotope shifts of the (13)C nuclear shielding in CX(2) (X = O, S, Se, Te) are calculated taking into account the relativistic spin-orbit (SO) interaction. The SO effect is considered for the first time for the secondary isotope shifts. The nuclear shielding hypersurface in terms of nuclear displacements is calculated by using a density-functional theory method. Ab initio multiconfiguration self-consistent field calculations are done at the equilibrium geometry for comparison. (13)C NMR measurements are carried out for CS(2). The calculated results are compared with both present and earlier experimental data on CO(2), CS(2), and CSe(2). The heavy-atom SO effects on the rovibrational corrections of (13)C shielding are shown to be significant. For CSe(2) and CTe(2), reliable prediction of secondary isotope effects and their temperature dependence requires the inclusion of the SO corrections. In particular, earlier discrepancies of theory and experiment for CSe(2) are fully resolved by taking the SO interactions into account.     

Ausstrahlungsproblem des Laplace-Operators für eine Klasse der Gebiete mit unbeschränktem Rand

In this paper we prove the existence and uniqueness of a solution u satisfying the equation- u – k² y = f (k , k 0), homogeneous Dirichlet data on the boundary and a radiation condition at infinity. We consider this problem in some unbounded region with an infinite boundary for which the assumption (x) · (x) 0 holds; here denotes the exterior normal and a given field.     

Marginal and simultaneous predictive classification using stratified graphical models

An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution. Several such rules have been recently considered and their asymptotic behavior has been characterized under the assumption that the observed features or variables used for building a classifier are conditionally independent given a simultaneous labeling of both the training samples and those from an unknown origin. Here we extend the theoretical results to predictive classifiers acknowledging feature dependencies either through graphical models or sparser alternatives defined as stratified graphical models. We show through experimentation with both synthetic and real data that the predictive classifiers encoding dependencies have the potential to substantially improve classification accuracy compared with both standard discriminative classifiers and the predictive classifiers based on solely conditionally independent features. In most of our experiments stratified graphical models show an advantage over ordinary graphical models.     

Synthesis of multivalent Streptococcus suis adhesion inhibitors by enzymatic cleavage of polygalacturonic acid and 'click' conjugation

A galabiose disaccharide building block was synthesized by an efficient pectinase cleavage of polygalacturonic acid and subsequent chemical functional group transformations. Besides the disaccharide, the corresponding trisaccharide was also obtained and modified. The compounds were subsequently conjugated to dendrimers with up to eight end groups using 'click' chemistry. The compounds were evaluated as inhibitors of adhesion of the pathogen Streptococcus suis in a hemagglutination assay and strong inhibition was observed for the tetra- and octavalent galabiose compound with MIC values in the low nanomolar range. The corresponding octavalent trisaccharide was a ca. 20-fold weaker inhibitor.     

High-performance liquid chromatography/electrospray ionization tandem mass spectrometry for characterization of enzymatic degradation of 2,2'-bis(2-oxazoline)-linked poly-epsilon-caprolactone

This paper describes a straightforward and rapid on-line characterization using high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ESI-MS(n)) of the enzymatic degradation products of 2,2'-bis(2-oxazoline)-linked poly-epsilon-caprolactone (PCL-O). These new PCL-O polymers are expected to be used in a variety of pharmaceutical and biomedical applications since they are degraded enzymatically by surface erosion. PCL-O was polymerized in a three-step reaction and characterized by (1)H-NMR and size-exclusion chromatography (SEC). Solvent cast polymer films were exposed to enzymatic degradation in phosphate buffer (pH 7.5, 1% pancreatin). The enzymatic degradation of the polymer produced a wide variety of water-soluble oligomers which were separated and identified by HPLC/ESI-MS(n). Optimization of the gradient HPLC method resulted in effective separation of the oligomers. Furthermore, specific structures of the oligomers were clearly identified by tandem mass spectrometry. According to these results, ester bonds seem to be most sensitive to enzymatic degradation and, correspondingly, pancreatic lipase seems to be mainly responsible for the enzymatic erosion of the PCL-O films. This novel mass spectrometric method provides important knowledge about the enzymatic degradation process and structure of the polymer which is difficult to ascertain by other conventional methods.     

Boron nitride nanotube-based amphiphilic hybrid nanomaterials for superior encapsulation of hydrophobic cargos

We report an organic-inorganic hybrid core-shell nanomaterial obtained by conjugation of an amphiphilic monomethoxy-poly(ethylene glycol)-b-poly(epsilon-caprolactone) diblock copolymer to hydroxylated boron nitride nanotubes (BNNTs). The extent of copolymer grafting reached 64% w/w, an exceptionally high value. The hybrid materials exhibit excellent physical stability in water and an outstanding loading capacity (31.3% w/w) for curcumin, a hydrophobic drug. Moreover, they present good compatibility with the Caco2 cell line, a model of intestinal epithelium. Our findings demonstrate the potential of multifunctional hybrid BNNTs to serve as a platform for complex amphiphilic nanoparticle architectures with improved features.     

Utilising thermoporometry to obtain new insights into nanostructured materials

Thermoporometry is a relatively new method of characterising porous properties of nanostructured materials based on observation of solid–liquid phase transitions of materials confined in pores. It provides several advantages over the conventional characterisation methods, mercury porosimetry and gas sorption. The advantages include possibility of using short measurement times, non-toxic chemicals and wet samples. In addition, complicated sample preparation and specialised instruments are not required. Therefore, it has a great potential of becoming a widely utilised characterisation method, although its potential has not yet been widely realised. In recent years, there has been a significant increase in research activities regarding the method. In the first part of the review, we introduce thermoporometry and review related results of the confinement effects on materials and their solid–liquid phase transition.     

Bayesian spatial modeling of genetic population structure

Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently. While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available, described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data sets illustrate well the benefits of incorporating spatial information to such an analysis.