Synthesis,characterization and hydrogen adsorption properties of metal–organic framework Al-TCBPB

In this work, a new metal–organic framework (MOF) was synthesized by using a large organic ligand 1,3,5-tris[4′-carboxy(1,1′-biphenyl)-4-yl] benzene (abbreviated as TCBPB) and aluminum as the metal that forms the secondary building unit (SBU) by solvothermal method. The MOF, named as Al-TCBPB, was characterized with pore textural properties, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Raman and FT-IR spectroscopy. Hydrogen adsorption was measured volumetrically at ambient pressure and temperatures of 77, 88 and 298 K and at high pressure (up to 9 MPa) for temperatures 77 and 298 K. Pore textural properties revealed a high BET surface area of 2311 m²/g, narrow bimodal pore widths of 11.8 Å and 20 Å and a total pore volume of 0.80 cm³/g. PXRD identified the crystal structure as monoclinic with space group c2/m. This MOF adsorbs 1.53 and 0.83 wt.% of hydrogen at 77 and 88 K, respectively, and pressures up to ambient conditions. At higher pressure of 9 MPa, it demonstrated an excess adsorption of 4.8 and 1.4 wt.% at 77 and 298 K, respectively; these high-pressure data fit well with modified Dubinin–Astakov (D–A) analytical model. The heat of adsorption values of Al-TCBPB vary between 5.9 and 4.9 kJ/mol for the hydrogen adsorption loading of 0.1–0.8 wt.% and decreases monotonically to approximately 2 kJ/mol when the adsorption loading becomes 4.8 wt%.     

Tensor scale: An analytic approach with efficient computation and applications

Scale is a widely used notion in computer vision and image understanding that evolved in the form of scale-space theory where the key idea is to represent and analyze an image at various resolutions. Recently, we introduced a notion of local morphometric scale referred to as “tensor scale” using an ellipsoidal model that yields a unified representation of structure size, orientation and anisotropy. In the previous work, tensor scale was described using a 2-D algorithmic approach and a precise analytic definition was missing. Also, the application of tensor scale in 3-D using the previous framework is not practical due to high computational complexity. In this paper, an analytic definition of tensor scale is formulated for n-dimensional (n-D) images that captures local structure size, orientation and anisotropy. Also, an efficient computational solution in 2- and 3-D using several novel differential geometric approaches is presented and the accuracy of results is experimentally examined. Also, a matrix representation of tensor scale is derived facilitating several operations including tensor field smoothing to capture larger contextual knowledge. Finally, the applications of tensor scale in image filtering and n-linear interpolation are presented and the performance of their results is examined in comparison with respective state-of-art methods. Specifically, the performance of tensor scale based image filtering is compared with gradient and Weickert’s structure tensor based diffusive filtering algorithms. Also, the performance of tensor scale based n-linear interpolation is evaluated in comparison with standard n-linear and windowed-sinc interpolation methods.     

A universal fluid cell for the imaging of biological specimens in the atomic force microscope

Recently, atomic force microscope (AFM) manufacturers have begun producing instruments specifically designed to image biological specimens. In most instances, they are integrated with an inverted optical microscope, which permits concurrent optical and AFM imaging. An important component of the set‐up is the imaging chamber, whose design determines the nature of the experiments that can be conducted. Many different imaging chamber designs are available, usually designed to optimize a single parameter, such as the dimensions of the substrate or the volume of fluid that can be used throughout the experiment. In this report, we present a universal fluid cell, which simultaneously optimizes all of the parameters that are important for the imaging of biological specimens in the AFM. This novel imaging chamber has been successfully tested using mammalian, plant, and microbial cells. Microsc. Res. Tech. 76:357–363, 2013. © 2013 Wiley Periodicals, Inc.     

Microstructural developments in TLP bonds using thin interlayers based on Ni–B coatings

Oxide dispersion strengthened alloy MA 758 was transient liquid phase (TLP) bonded using thin interlayers based on Ni–B electrodeposited coatings and the microstructural developments across the joint region were studied. The bonding surfaces were electrodeposited with a coat thickness of 2–9 μm and microstructural features were characterized by scanning electron microscopy and energy dispersive X-ray spectroscopy. The homogeneity of the joint was assessed performing micro-hardness test. The results showed that the coating thickness as well as the amount of melting point depressants (boron) in the coatings had a significant effect on the microstructural developments within the joint region. TLP bonds made using a 2 μm thick coating interlayer produced a joint with no visible precipitate formation and parent metal dissolution, and the absence of precipitates was attributed to the lower volume concentration of boron in the 2 μm thick coating interlayer.     

Vectorial scale-based fuzzy-connected image segmentation

This paper presents an extension of previously published theory and algorithms for fuzzy-connected image segmentation. In this approach, a strength of connectedness is assigned to every pair of image elements. This is done by finding the strongest among all possible connecting paths between the two elements in each pair. The strength assigned to a particular path is defined as the weakest affinity between successive pairs of elements along the path. Affinity specifies the degree to which elements hang together locally in the image. A scale is determined at every element in the image that indicates the size of the largest homogeneous hyperball region centered at the element. In determining affinity between any two elements, all elements within their scale regions are considered. This method has been effectively utilized in several medical applications. In this paper, we generalize this method from scalar images to vectorial images. In a vectorial image, scale is defined as the radius of the largest hyperball contained in the same homogeneous region under a predefined condition of homogeneity of the image vector field. Two different components of affinity, namely homogeneity-based affinity and object-feature-based affinity, are devised in a fully vectorial manner. The original relative fuzzy connectedness algorithm is utilized to delineate a specified object via a competing strategy among multiple objects. We have presented several studies to evaluate the performance of this method based on simulated MR images, 20 clinical MR images, and 250 mathematical phantom images. These studies indicate that the fully vectorial fuzzy connectedness formulation has generally overall better accuracy than the method using some intermediate ad hoc steps to fit the vectorial image to a scalar fuzzy connectedness formulation, and precision and efficiency are similar for these two methods.     

Use of ridge regression for improved estimation of kinetic constants from PET data

The estimation of parameters in radio-tracer models from positron emission tomography (PET) data by nonlinear least squares (NLS) often leads to results with unacceptable mean square error (ME) characteristics. The introduction of constraints on parameters has the potential to address this problem. We examine a ridge-regression technique that augments the standard NLS criterion by the addition of a term which penalizes estimates which deviate from physiologically reasonable values. A variation on a plug-in methodology of Hoerl et al. [7] is examined for data-dependent selection of the degree of reliance to place on the penalizing term. A simulation study is carried out to evaluate the performance of this approach in the context of estimation of kinetic constants in the three-compartment model used to analyze data from PET studies with fluoro-deoxyglucose (FDG). Results show that over a range of realistic noise levels, the ridge-regression procedure can be expected to reduce the root ME of parameter estimates by 60%. This result is not found to be substantially dependent on the precise formulation of the penalty function used. Thus, the use of ridge regression for estimation of kinetic parameters in PET studies is considered to be a promising tool.     

On the nature and structure of ‘niobic acid’ and its pyrolytic products: 1H NMR,I.R., conductivity and Ion exchange studies

Niobic acid of composition H₈Nb₆O₁₉ has been prepared by thorough drying of the hydrated oxide at 150°C. The mass was heated in stages up to 1200°C and the intermediate products were subjected to ¹H NMR measurements for determining their proton content. Infra red spectra indicate the presence of protons which are highly delocalised among different oxygen sites. The electrical conductivity of niobic acid has been interpreted as due to proton-hopping. The ion exchange capacity of the material has also been studied. The data have been used to formulate the structure of niobic acid and to obtain the correct procedure for the gravimetric determination of niobium as oxide.     

On the nature and structure of ‘niobic acid’ and its pyrolytic products: Part III: Correlation among the soluble niobates,the stoichiometric and the nonstoichiometric mixed oxides of niobium

Innumerable variety of metal niobates containing diverse M₂O: Nb₂O₅ ratios are reported in the literature but three definite species could only be recognised — ortho, meta and the normal hexaniobates. These three types have been correlated and are shown to be the derivatives of the isopolyniobic acid. The stoichiometric mixed niobates can be derived from the metaniobates and their facile transformation to the perovskite type of lattice has been explained. The nonstoichiometric mixed niobates with cubic perovskite structure exhibit ‘bronze’ properties and their compositions are derived from the structure of the parent metaniobate. All these formulations agree with the compositions and the properties of known species.     

Artery-vein separation via MRA--an image processing approach

This paper presents a near-automatic process for separating vessels from background and other clutter as well as for separating arteries and veins in contrast-enhanced magnetic resonance angiographic (CE-MRA) image data, and an optimal method for three-dimensional visualization of vascular structures. The separation process utilizes fuzzy connected object delineation principles and algorithms. The first step of this separation process is the segmentation of the entire vessel structure from the background and other clutter via absolute fuzzy connectedness. The second step is to separate artery from vein within this entire vessel structure via iterative relative fuzzy connectedness. After seed voxels are specified inside artery and vein in the CE-MRA image, the small regions of the bigger aspects of artery and vein are separated in the initial iterations, and further detailed aspects of artery and vein are included in later iterations. At each iteration, the artery and vein compete among themselves to grab membership of each voxel in the vessel structure based on the relative strength of connectedness of the voxel in the artery and vein. This approach has been implemented in a software package for routine use in a clinical setting and tested on 133 CE-MRA studies of the pelvic region and two studies of the carotid system from six different hospitals. In all studies, unified parameter settings produced correct artery-vein separation. When compared with manual segmentation/separation, our algorithms were able to separate higher order branches, and therefore produced vastly more details in the segmented vascular structure. The total operator and computer time taken per study is on the average about 4.5 min. To date, this technique seems to be the only image processing approach that can be routinely applied for artery and vein separation.