Effects of oxidante acid treatments on carbon-templated hierarchical SAPO-11 materials: synthesis, characterization and catalytic evaluation in n-decane hydroiosomerization

Hierarchical SAPO-11 was synthesized using a commercial Merck carbon as template. Oxidant acid treatments were performed on the carbon matrix in order to investigate its influence on the properties of SAPO-11. Structural, textural and acidic properties of the different materials were evaluated by XRD, SEM, N-2 adsorption, pyridine adsorption followed by IR spectroscopy and thermal analyses. The catalytic behavior of the materials (with 0.5 wt.% Pt, introduced by mechanic mixture with Pt/Al2O3), were studied in the hydroisomerization of n-decane. The hierarchical samples showed higher yields in monobranched isomers than typical microporous SAPO-11, as a direct consequence of the modification on both porosity and acidity, the later one being the most predominant. (C) 2014 Elsevier B.V. All rights reserved.

Halogen-bonded tris (2,4-Bis(trichloromethyl)-1,3,5,triazapentadienato)-M(III) [M = Mn, Fe, Co] complexes and their catalytic activity in the peroxidative oxidation of 1-phenylethanol to acetophenone

One-pot template condensation of CCl3C=N with ammonia on a metal source [MnCl2 center dot 4H(2)O, FeCl3 center dot 6H(2)O or Co(CH3COO)(2)center dot 4H(2)O] in DMSO led to the formation of tris(2,4-bis(trichloromethyl)-1,3,5-triazapentadienato)-M(III) complexes, [M(NH=C(CCl3)NC(CCl3)=-NH}(3)]center dot n(CH3)(2)SO [M = Mn, n = 1 (1); M = Fe, n = 2 (2); M = Co, n = 2 (3)1, which were characterized using elemental analysis, and IR, ESI-MS and single-crystal X-ray analysis. The role of inter- and intramolecular non-covalent halogen and hydrogen bonds in the synthesis of 1-3 is discussed. It is shown that the crystal ionic radii of the metal ions [68.5 (Co) < 69 (Fe) < 72 (Mn), pm] are related to the corresponding Cl center dot center dot center dot Cl distances [3.178 (3) > 3.155 (2) > 3.133 (1) Al. Compounds 1-3 and the related di(triazapentadienato)-Cu(v) complex [Cu(NH=C(CCl3)NC(CCl3)=NH}2]center dot 2(CH3)(2)SO (4) act as catalyst precursors for the additive-free microwave (MW) assisted homogeneous oxidation of 1-phenylethanol with tert-butylhydroperoxide (TBHP), leading to the formation of acetophenone with yields up to 99% and TONs up to 5.0 x 10(3) after 1 h of low power (10 W) MW irradiation.

Mu-Chlorido-Bridged Dimanganese(II) complexes of the schiff base derived from [2+2] condensation of 2,6-diformyl-4-methylphenol and 1,3-bis(3-aminopropyl)tetramethyldisloxane: structure, magnetismo, electrochemical behaviour, and catalytic oxidation of secondary alcohols

The reaction of 2,6-diformyl-4-methylphenol with 1,3-bis(3-aminopropyl)tetramethyldisiloxane in the presence of MnCl2 in a 1:1:2 molar ratio in methanol afforded a dinuclear -chlorido-bridged manganese(II) complex of the macrocyclic [2+2] condensation product (H2L), namely, [Mn2Cl2(H2L)(HL)]Cl center dot 3H(2)O (1). The latter afforded a new compound, namely, [Mn2Cl2(H2L)(2)][MnCl4]center dot 4CH(3)CN center dot 0.5CHCl(3 center dot)0.4H(2)O (2), after recrystallisation from 1:1 CHCl3/CH3CN. The co-existence of the free and complexed azomethine groups, phenolato donors, mu-chlorido bridges, and the disiloxane unit were well evidenced by ESI mass spectrometry and FTIR spectroscopy and confirmed by X-ray crystallography. The magnetic measurements revealed an antiferromagnetic interaction between the two high-spin (S = 5/2, g = 2) manganese(II) ions through the mu-chlorido bridging ligands. The electrochemical behaviour of 1 and 2 has been studied, and details of their redox properties are reported. Both compounds act as catalysts or catalyst precursors in the solvent-free low-power microwave-assisted oxidation of selected secondary alcohols, for example, 1-phenylethanol, cyclohexanol, 2- and 3-octanol, to the corresponding ketones in the absence of solvent. The highest yield of 72% was achieved for 1-phenylethanol by using a maximum of 1% molar ratio of catalyst relative to substrate.

µ-Chlorido-bridged dimanganese(II) complexes of the schiff base derived from [2+2] condensation of 2,6-diformyl-4-methylphenol and 1,3-bis(3-aminopropyl)tetramethyldisiloxane: structure, magnetismo, electrochemical behaviour, and catalytic oxidation of secondary alcohols

The reaction of 2,6-diformyl-4-methylphenol with 1,3-bis(3-aminopropyl)tetramethyldisiloxane in the presence of MnCl2 in a 1:1:2 molar ratio in methanol afforded a dinuclear -chlorido-bridged manganese(II) complex of the macrocyclic [2+2] condensation product (H2L), namely, [Mn2Cl2(H2L)(HL)]Cl center dot 3H(2)O (1). The latter afforded a new compound, namely, [Mn2Cl2(H2L)(2)][MnCl4]center dot 4CH(3)CN center dot 0.5CHCl(3 center dot)0.4H(2)O (2), after recrystallisation from 1:1 CHCl3/CH3CN. The co-existence of the free and complexed azomethine groups, phenolato donors, mu-chlorido bridges, and the disiloxane unit were well evidenced by ESI mass spectrometry and FTIR spectroscopy and confirmed by X-ray crystallography. The magnetic measurements revealed an antiferromagnetic interaction between the two high-spin (S = 5/2, g = 2) manganese(II) ions through the mu-chlorido bridging ligands. The electrochemical behaviour of 1 and 2 has been studied, and details of their redox properties are reported. Both compounds act as catalysts or catalyst precursors in the solvent-free low-power microwave-assisted oxidation of selected secondary alcohols, for example, 1-phenylethanol, cyclohexanol, 2- and 3-octanol, to the corresponding ketones in the absence of solvent. The highest yield of 72% was achieved for 1-phenylethanol by using a maximum of 1% molar ratio of catalyst relative to substrate.

Characterisation and electrochemical behaviour of electrodeposited Cu-Fe foams applied as pseudocapacitor electrodes

Copper iron (Cu-Fe) 3D porous foams for supercapacitor electrodes were electrodeposited in the cathodic regime, on stainless steel current collectors, using hydrogen bubbling dynamic template. The foams were prepared at different current densities and deposition times. The foams were submitted to thermal conditioning at temperatures of 150 and 250 degrees C. The morphology, composition and structure of the formed films were studied by SEM, EDS and XRD, respectively. The electrochemical behaviour was studied by cyclic voltammetry, electrochemical impedance spectroscopy and chronopotentiometry. The morphology of the 3D Cu-Fe foams is sensitive to the electrodeposition current and time. The increase of the current density produces a denser, larger and more ramified dendritic structure. Thermal conditioning at high temperature induces a coarser grain structure and the formation of copper oxides, which affect the electrochemical behaviour. The electrochemical response reveals the presence of various redox peaks assigned to the oxidation and reduction of Cu and Fe oxides and hydroxides in the foams. The specific capacitance of the 3D Cu Fe foams was significantly enhanced by thermal conditioning at 150 degrees C. The highest specific capacitance values attained 297 Fg(-1) which are much above the ones typically observed for single Cu or Fe Oxides and hydroxides. These values highlight a synergistic behaviour resulting from the combination of Cu and Fe in the form of nanostructured metallic foams. Moreover, the capacitance retention observed in an 8000 charge/discharge cycling test was above 66%, stating the good performance of these materials and its enhanced electrochemical response as supercapacitor negative electrodes. (C) 2014 Elsevier B.V. All rights reserved.

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Hydrogen evolution on nanostructured Ni-Cu foams

Three-dimensional (3D) nickel-copper (Ni-Cu) nanostructured foams were prepared by galvanostatic electrodeposition, on stainless steel substrates, using the dynamic hydrogen bubble template. These foams were tested as electrodes for the hydrogen evolution reaction (HER) in 8 M KOH solutions. Polarisation curves were obtained for the Ni-Cu foams and for a solid Ni electrode, in the 25-85 degrees C temperature range, and the main kinetic parameters were determined. It was observed that the 3D foams have higher catalytic activity than pure Ni. HER activation energies for the Ni-Cu foams were lower (34-36 kJ mol(-1)) than those calculated for the Ni electrode (62 kJ mol(-1)). The foams also presented high stability for HER, which makes them potentially attractive cathode materials for application in industrial alkaline electrolysers.

Mn-II and Cu-II complexes with arylhydrazones of active methylene compounds as effective heterogeneous catalysts for solvent- and additive-free microwave-assisted peroxidative oxidation of alcohols

A one-pot template reaction of sodium 2-(2-(dicyanomethylene) hydrazinyl) benzenesulfonate (NaHL1) with water and manganese(II) acetate tetrahydrate led to the mononuclear complex [Mn(H2O)(6)](HL1a)(2)center dot 4H(2)O (1), where (HL1a) -= 2-(SO3-)C6H4(NH)=N=C(C N) (CONH2) is the carboxamide species derived from nucleophilic attack of water on a cyano group of (HL1) . The copper tetramer [Cu-4(H2O)(10)(-) (1 kappa N: kappa O-2: kappa O, 2 kappa N: k(O)-L-2)(2)]center dot 2H(2)O (2) was obtained from reaction of Cu(NO3)(2)center dot 2.5H(2)O with sodium 5-(2( 4,4-dimethyl-2,6-dioxocyclohexylidene) hydrazinyl)-4-hydroxybenzene-1,3-disulfonate (Na2H2L2). Both complexes were characterized by elemental analysis, IR spectroscopy, ESI-MS and single crystal X-ray diffraction. They exhibit a high catalytic activity for the solvent-and additive-free microwave (MW) assisted oxidation of primary and secondary alcohols with tert-butylhydroperoxide, leading to yields of the oxidized products up to 85.5% and TOFs up to 1.90 x 103 h(-1) after 1 h under low power (5-10 W) MW irradiation. Moreover, the heterogeneous catalysts are easily recovered and reused, at least for three consecutive cycles, maintaining 89% of the initial activity and a high selectivity.

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.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. 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. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

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