Monometallic salts derived from complex anions of group 10 metal ions with p-tolylsulfonyldithiocarbimate ligand: Synthesis, characterization and properties

New monometallic complex salts of the form X-2[M(L)(2)] [M = Ni2+, X = (CH3)(2)NH2+(1); M = Ni2+, X = (CH3)(4)N+ (2); M = Ni2+, X = (C2H5)(4)N+(3); M = Ni2+, X = (C3H7)(4)N+(4); M = Ni2+; X = (C6H13)(4)N+) (5); M = Pd2+,X = (CH3)(2)NH2+(6); M = Pd2+, X= (C2H5)(4)N+(7); M = Pd2+, X= (C3H7)(4)N+(8); M = Pd2+, X = (C6H13)(4)N+ (9); M = Pt2+, X = (CH3)(2)NH2+(10); L = p-tolylsulfonyldithiocarbimate (CH3C6H4SO2N=CS22 )] have been prepared and characterized by elemental analysis, IR, H-1 and C-13 NMR and UV-Vis spectroscopy; 1, 3, 4 and 5 by X-ray crystallography. In 1, 3, 4 and 5, the Ni atom is four coordinate with a square planar environment being bonded to four sulfur atoms from two bidentate ligands. All the salts are weakly conducting (sigma(rt) = 10 (7) to 10 (14) Scm (1)) because of the lack of significant S center dot center dot center dot S intermolecular interactions between complex anions [M(L)(2)](2) in the solid state however, they show behavior of semiconductors in the temperature range 353-453 K. All the Pd(II) and Pt(II) salts exhibited phtotolumeniscent emissions near visible region in solution at room temperature.

Salts responsive nanovesicles through π-stacking induced self-assembly of backbone modified tripeptides

A set of backbone modified peptides of general formula Boc-Xx-m-ABA-Yy-OMe where m-ABA is meta-aminobenzoic acid and Xx and Yy are natural amino acids such as Phe, Gly, Pro, Leu, Ile, Tyr and Trp etc., are found to self-assemble into soft nanovesicular structures in methanol-water solution (9:1 by v/v). At higher concentration the peptides generate larger vesicles which are formed through fusion of smaller vesicles. The formation of vesicles has been facilitated through the participation of various noncovalent interactions such as aromatic pi-stacking, hydrogen bonding and hydrophobic interactions. Model study indicates that the pi-stacking induced self-assembly, mediated by m-ABA is essential for well structured vesicles formation. The presence of conformationally rigid m-ABA in the backbone of the peptides also helps to form vesicular structures by restricting the conformational entropy. The vesicular structures get disrupted in presence of various salts such as KCl, CaCl(2), N(n-Bu)(4)Br and (NH(4))(2)SO(4) in methanol-water solution. Fluorescence microscopy and UV studies reveal that the soft nanovesicles encapsulate organic dye molecules such as Rhodamine B and Acridine Orange which could be released through salts induced disruption of vesicles.

A very rare self-assembled ribbon of fused cyclic water pentamers encapsulated in a Cu-II based metal-organic framework

A metal organic framework of Cu-II, tartarate (tar) and 2,2'-bipyridyl (2,2'-bipy)], {[Cu(tar)(2,2'-bipy)]center dot 5H(2)O}(n)} (1) has been synthesized at the mild ambient condition and characterized by single crystal X-ray crystallography. In the compound, the Cu(2,2'-bipy) entities are bridged by tartarate ions which are coordinated to Cu-II by both hydroxyl and monodentate carboxylate oxygen to form a one-dimensional chain. The non-coordinated water molecules form ID water chains by edge-sharing cyclic water pentamers along with dangling water dimers. It shows reversible water expulsion upon heating. The water chains join the ID coordination polymeric chains to a 31) network through hydrogen-bond interactions.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.