Periodic hexagonal mesostructured chalcogenides based on platinum and [SnSe4]4- and [SnTe4]4- precursors. Solvent dependence of nanopore and wall organization

Mesostructured chalcogenide-based materials with long-range order and semiconducting properties can be prepared using suitable molecular building blocks, linkage metal ions and surfactant molecules. In this paper we present surfactant templated, open framework platinum tin selenide and telluride materials assembled using K4SnQ4 (Q = Se, Te) salts and K2PtCl4 as precursors and a study of pore and wall organization. We find that materials prepared in water exhibit disordered pore organization, whereas those prepared in formamide are long-range ordered with hexagonal symmetry. In formamide the [SnQ4]4- anions undergo condensation-oligomerization reactions that produce different chalcogenido molecular species, whereas in water the anions remain intact. In addition to solvent, the pore organization and overall quality of the mesostructured materials strongly depend on the surfactant molecules, i.e., chain length and headgroup size. For example, highly ordered mesostructured platinum tin selenides with hexagonal symmetry were obtained using the hydroxyl-functionalized surfactants CnH2n+1N(CH3)(CH2CH2OH)2Br (n = 16, 18, and 20), but when the headgroup was triethylammonium, hexagonal pore order was achieved only for n = 20 and not for n = 16 and 18. The experimental results imply that in order to achieve highly ordered chalcogenide frameworks a single building anionic block might be insufficient. Finally, we also report the first examples of hexagonal mesostructured Pt/Sn/Te materials based on K4SnTe4 as the precursor. The tellurides behave differently for their selenium analogues and have very low energy band gaps, in the range 0.5-0.7 eV.     

Equilibrium points of the tilted perfect fluid Bianchi VI<Subscript><Emphasis Type="Italic">h</Emphasis></Subscript> state space

We present the full set of evolution equations for the spatially homogeneous cosmologies of type VI_h filled with a tilted perfect fluid and we provide the corresponding equilibrium points of the resulting dynamical state space. It is found that only when the group parameter satisfies h > –1 a self-similar solution exists. In particular we show that for h > – there exists a self-similar equilibrium point provided that whereas for h < – the state parameter belongs to the interval (1, . This family of new exact self-similar solutions belongs to the subclass n = 0 having non-zero vorticity. In both cases the equilibrium points have a six-dimensional stable manifold and may act as future attractors at least for the models satisfying n = 0. Also we give the exact form of the self-similar metrics in terms of the state and group parameter. As an illustrative example we provide the explicit form of the corresponding self-similar radiation model ( = ), parametrised by the group parameter h. Finally we show that there are no tilted self-similar models of type III and irrotational models of type VI_h.     

Quantitative combination of volumetric MR imaging and MR spectroscopy data for the discrimination of meningiomas from metastatic brain tumors by means of pattern recognition

The analysis of information derived from magnetic resonance imaging (MRI) and spectroscopy (MRS) has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to investigate the efficiency of the combination of textural MRI features and MRS metabolite ratios by means of a pattern recognition system in the task of discriminating between meningiomas and metastatic brain tumors. The data set consisted of 40 brain MR image series and their corresponding spectral data obtained from patients with verified tumors. The pattern recognition system was designed employing the support vector machines classifier with radial basis function kernel; the system was evaluated using an external cross validation process to render results indicative of the generalization performance to “unknown” cases. The combination of MR textural and spectroscopic features resulted in 92.15% overall accuracy in discriminating meningiomas from metastatic brain tumors. The fusion of the information derived from MRI and MRS data might be helpful in providing clinicians a useful second opinion tool for accurate characterization of brain tumors.     

Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images

In this study, a pattern recognition system has been developed for the discrimination of multiple sclerosis (MS) from cerebral microangiopathy (CM) lesions based on computer-assisted texture analysis of magnetic resonance images. Twenty-three textural features were calculated from MS and CM regions of interest, delineated by experienced radiologists on fluid attenuated inversion recovery images and obtained from 11 patients diagnosed with clinically definite MS and from 18 patients diagnosed with clinically definite CM. The probabilistic neural network classifier was used to construct the proposed pattern recognition system and the generalization of the system to unseen data was evaluated using an external cross validation process. According to the findings of the present study, statistically significant differences exist in the values of the textural features between CM and MS: MS regions were darker, of higher contrast, less homogeneous and rougher as compared to CM.     

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.     

Ricci and Matter Collineations of Locally Rotationally Symmetric Space-Times

A new method is presented for the determination of Ricci Collineations (RC) and Matter Collineations (MC) of a given spacetime, in the cases where the Ricci tensor and the energy momentum tensor are non-degenerate and have a similar form with the metric. This method reduces the problem of finding the RCs and the MCs to that of determining the KVs whereas at the same time uses already known results on the motions of the metric. We employ this method to determine all hypersurface homogeneous locally rotationally symmetric spacetimes, which admit proper RCs and MCs. We also give the corresponding collineation vectors. These results conclude a long due open problem, which has been considered many times partially in the literature.     

Probing the structure and energetics of dislocation cores in SiGe alloys through Monte Carlo simulations

We present a methodology for the investigation of dislocation energetics in segregated alloys based on Monte Carlo simulations which equilibrate the topology and composition of the dislocation core and its surroundings. An environment-dependent partitioning of the system total energy into atomic contributions allows us to link the atomistic picture to continuum elasticity theory. The method is applied to extract core energies and radii of 60 degrees glide dislocations in segregated SiGe alloys which are inaccessible by other methods.     

Water confined in two-dimensions: Fundamentals and applications

The behavior of water in close proximity to other materials under ambient conditions is of great significance due to its importance in a broad range of daily applications and scientific research. The structure and dynamics of water at an interface or in a nanopore are often significantly different from those of its bulk counterpart. Until recently, experimental access to these interfacial water structures was difficult to realize. The advent of two-dimensional materials, especially graphene, and the availability of various scanning probe microscopies were instrumental to visualize, characterize and provide fundamental knowledge of confined water. This review article summarizes the recent experimental and theoretical progress in a better understanding of water confined between layered Van der Waals materials. These results reveal that the structure and stability of the hydrogen bonded networks are determined by the elegant balance between water-surface and water-water interactions. The water-surface interactions often lead to structures that differ significantly from the conventional bilayer model of natural ice. Here, we review the current knowledge of water adsorption in different environments and intercalation within various confinements. In addition, we extend this review to cover the influence of interfacial water on the two-dimensional material cover and summarize the use of these systems in potential novel applications. Finally, we discuss emerged issues and identify some flaws in the present understanding.     

Distortion product otoacoustic emissions provide clues hearing mechanisms in the frog ear

2f1-f2 and 2 f2-f1 distortion product otoacoustic emissions (DPOAEs) were recorded from both ears of male and female Rana pipiens pipiens and Rana catesbeiana. The input-output (I/O) curves obtained from the amphibian papilla (AP) of both frog species are analogous to I/O curves recorded from mammals suggesting that, similarly to the mammalian cochlea, there may be an amplification process present in the frog AP. DPOAE level dependence on L1-L2 is different from that in mammals and consistent with intermodulation distortion expectations. Therefore, if a mechanical structure in the frog inner ear is functioning analogously to the mammalian basilar membrane, it must be more broadly tuned. DPOAE audiograms were obtained for primary frequencies spanning the animals' hearing range and selected stimulus levels. The results confirm that DPOAEs are produced in both papillae, with R. catesbeiana producing stronger emissions than R. p. pipiens. Consistent with previously reported sexual dimorphism in the mammalian and anuran auditory systems, females of both species produce stronger emissions than males. Moreover, it appears that 2 f1-f2 in the frog is generated primarily at the DPOAE frequency place, while 2 f2-f1 is generated primarily at a frequency place around the primaries. Regardless of generation place, both emissions within the AP may be subject to the same filtering mechanism, possibly the tectorial membrane.     

Post-column labeling techniques in amino acid analysis by liquid chromatography

Amino acid analysis (AAA) has always presented an analytical challenge in terms of sample preparation, separation, and detection. Because of the vast number of amino acids, various separation methods have been applied taking into consideration the large differences in their chemical structures, which span from nonpolar to highly polar side chains. Numerous separation methods have been developed in the past 60 years, and impressive achievements have been made in the fields of separation, derivatization, and detection of amino acids (AAs). Among the separation methods, liquid chromatography (LC) prevailed in the AAA field using either pre-column or post-column labeling techniques in order to improve either separation of AAs or selectivity and sensitivity of AAA. Of the two approaches, the post-column technique is a more rugged and reproducible method and provides excellent AAs separation relatively free from interferences. This review considers current separations combined with post-column labeling techniques for AAA, comparison with the pre-column methods, and the strategies used to develop effective post-column methodology. The focus of the article is on LC methods coupled with post-column labeling techniques and studying the reactions to achieve optimum post-column derivatization (PCD) conditions in order to increase sensitivity and selectivity using various types of detectors (UV–Vis, fluorescence, electrochemical etc.) and illustrating the versatility of the PCD methods for practical analysis.