Pt/Carbon materials as Bi-Functional catalysts for N-decane hydroisomerization

The activity and selectivity of bi-functional carbon-supported platinum catalysts for the hydroisomerization of n-alkanes have been studied. The influence of the properties of the carbon support on the performance of the catalysts were investigated by incorporating the metallic function on a series of carbons with varied porosity (microporous: GL-50 from Norit, and mesoporous: CMK-3) and surface chemistry (modified by wet oxidation). The characterization results achieved with H-2 chemisorption and TEM showed differences in surface metal concentrations and metal-support interactions depending on the support composition. The highest metal dispersion was achieved after oxidation of the carbon matrix in concentrated nitric acid, suggesting that the presence of surface functional sites distributed in inner and outer surface favors a homogeneous metal distribution. On the other hand, the higher hydrogenating activity of the catalysts prepared with the mesoporous carbon pointed out that a fast molecular traffic inside the pores plays an important role in the catalysts performance. For n-decane hydroisomerization of long chain n-alkanes, higher activities were obtained for the catalysts with an optimized acidity and metal dispersion along with adequate porosity, pointing out the importance of the support properties in the performance of the catalysts.

Autonomic Activities in the Execution of Scientific Workflows: Evaluation of the AWARD Framework

Workflows have been successfully applied to express the decomposition of complex scientific applications. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from tasks specification, decentralizing the control of workflow activities allowing their tasks to run in distributed infrastructures, and supporting dynamic workflow reconfigurations. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on Process Networks, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures. Each AWA executes a task developed as a Java class with a generic interface allowing end-users to code their applications without low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables dynamic workflow reconfiguration. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to the Amazon (Elastic Computing EC2) Cloud.

Synthesis of titanate nanofibers co-sensitized with ZnS and Bi2S3 nanocrystallites and their application on pollutants removal

The synthesis of nanocomposite materials combining titanate nanofibers (TNF) with nanocrystalline ZnS and Bi2S3 semiconductors is described in this work. The TNF were produced via hydrothermal synthesis and sensitized with the semiconductor nanoparticles, through a single-source precursor decomposition method. ZnS and Bi2S3 nanoparticles were successfully grown onto the TNF's surface and Bi2S3-ZnS/TNF nanocomposite materials with different layouts. The samples' photocatalytic performance was first evaluated through the production of the hydroxyl radical using terephthalic acid as probe molecule. All the tested samples show photocatalytic ability for the production of this oxidizing species. Afterwards, the samples were investigated for the removal of methylene blue. The nanocomposite materials with best adsorption ability were the ZnS/TNF and Bi2S3ZnS/TNF. The dye removal was systematically studied, and the most promising results were obtained considering a sequential combination of an adsorption-photocatalytic degradation process using the Bi2S3ZnS/TNF powder as a highly adsorbent and photocatalyst material. (C) 2015 Elsevier Ltd. All rights reserved.

Novel coordination polymers with (Pyrazolato)-based tectons: catalytic activity in the peroxidative oxidation of alcohols and cyclohexane

Coupling five rigid or flexible bis(pyrazolato)based tectons with late transition metal ions allowed us to isolate 18 coordination polymers (CPs). As assessed by thermal analysis, all of them possess a remarkable thermal stability, their decomposition temperatures lying in the range of 340-500 degrees C. As demonstrated by N-2 adsorption measurements at 77 K, their Langmuir specific surface areas span the rather vast range of 135-1758 m(2)/g, in agreement with the porous or dense polymeric architectures retrieved by powder X-ray diffraction structure solution methods. Two representative families of CPs, built up with either rigid or flexible spacers, were tested as catalysts in (0 the microwave-assisted solvent-free peroxidative oxidation of alcohols by t-BuOOH, and (ii) the peroxidative oxidation of cydohexane to cydohexanol and cydohexanone by H2O2 in acetonitrile. Those CPs bearing the rigid spacer, concurrently possessing higher specific surface areas, are more active than the corresponding ones with the flexible spacer. Moreover, the two copper(I)-containing CPs investigated exhibit the highest efficiency in both reactions, leading selectively to a maximum product yield of 92% (and TON up to 1.5 x 10(3)) in the oxidation of 1-phenylethanol and of 11% in the oxidation of cydohexane, the latter value being higher than that granted by the current industrial process.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Blind hyperspectral unmixing

Hyperspectral unmixing methods aim at the decomposition of a hyperspectral image into a collection endmember signatures, i.e., the radiance or reflectance of the materials present in the scene, and the correspondent abundance fractions at each pixel in the image. This paper introduces a new unmixing method termed dependent component analysis (DECA). This method is blind and fully automatic and it overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. DECA is based on the linear mixture model, i.e., each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abundances are modeled as mixtures of Dirichlet densities, thus enforcing the non-negativity and constant sum constraints, imposed by the acquisition process. The endmembers signatures are inferred by a generalized expectation-maximization (GEM) type algorithm. The paper illustrates the effectiveness of DECA on synthetic and real hyperspectral images.