Publications

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

In this work, an efficient nanocatalyst was developed based on nanoadsorbent beads. Herein, carboxymethyl cellulose-copper oxide-cobalt oxide nanocomposite beads (CMC/CuO-Co₂O₃) crosslinked by using AlCl₃ were successfully prepared. The beads were then coated with chitosan (Cs), Cs@CMC/CuO-Co₂O_3. The prepared beads, CMC/CuO-Co₂O₃ and Cs@CMC/CuO-Co₂O₃, were utilized as adsorbents for heavy metal ions (Ni, Fe, Ag and Zn). By using CMC/CuO-Co₂O₃ and Cs@CMC/CuO-Co₂O₃, the distribution coefficients (K_d) for Ni, Fe, Ag and Zn were (41.166 and 6173.6 mLg^-1), (136.3 and 1500 mLg^-1), (20,739.1 and 1941.1 mLg^-1) and (86.9 and 2333.3 mLg^-1), respectively. Thus, Ni was highly adsorbed by Cs@CMC/CuO-Co₂O₃ beads. The metal ion adsorbed on the beads were converted into nanoparticles by treating with reducing agent (NaBH₄) and named Ni/Cs@CMC/CuO-Co₂O₃. Further, the prepared nanoparticles-decorated beads (Ni/Cs@CMC/CuO-Co₂O₃) were utilized as nanocatalysts for the reduction of organic and inorganic pollutants (4-nitophenol, MO, EY dyes and potassium ferricyanide K₃[Fe(CN)₆]) in the presence of NaBH₄. Among all catalysts, Ni/Cs@CMC/CuO-Co₂O₃ had the highest catalytic activity toward MO, EY and K₃[Fe(CN)₆], removing up to 98% in 2.0 min, 90 % in 6.0 min and 91% in 6.0 min, respectively. The reduction rate constants of MO, EY, 4-NP and K₃[Fe(CN)₆] were 1.06 × 10^-1, 4.58 × 10^-3, 4.26 × 10^-3 and 5.1 × 10^-3 s^-1, respectively. Additionally, the catalytic activity of the Ni/Cs@CMC/CuO-Co₂O₃ beads was effectively optimized. The stability and recyclability of the beads were tested up to five times for the catalytic reduction of MO, EY and K₃[Fe(CN)₆]. It was confirmed that the designed nanocomposite beads are ecofriendly and efficient with high strength and stability as catalysts for the reduction of organic and inorganic pollutants.

Chemical sensors are needed to develop efficient sensing systems with high flexibility, and low capital cost for controlled recognition of analytes. Herein, we report a highly sensitive, low cost, simple chemical sensor based on flower shape and hollow sphere CuO. Following the precipitation process, FESEM images revealed that CuO nanosheets are grown in high density and organized in a proper manner to give a flower shape structure; however, following the hydrothermal method in the presence of urea, the cage like micro structures CuO hollow spheres have been discovered. XRD revealed that the grown CuO has a single-crystalline phase of a monoclinic system. The resistivity of CuO hollow spheres (1.93 × 106 Ω m) is ∼100 times higher than flower shape CuO (2.2 × 104 Ω m). The prepared CuO flower shapes and hollow spheres have been evaluated for the detection and quantification of phenyl hydrazine. The findings indicate that CuO hollow spheres and flowers exhibited good sensitivity (0.578 and 7.145 μA cm−2 mM−1) and a lower limit of detection (LOD = 2.4 mM) with a linear dynamic range (LDR) of 5.0 μM to 10.0 mM and rapid assessment of the reaction kinetics (in the order of seconds). The designed flower shape CuO sensing system is 12 times more sensitive than CuO hollow spheres. To the best of our knowledge, the measured sensitivity ∼ 7.145 μA cm−2 mM−1 of CuO flower shapes is found to be among the highest sensitivity values reported for phenyl hydrazine up to now.

Over the years, a number of dimensionality reduction techniques have been proposed and used in chemoinformatics to perform nonlinear mappings. In this study, four representatives of nonlinear dimensionality reduction methods related to two different families were analyzed: distance-based approaches (Isomap and Diffusion Maps) and topology-based approaches (Generative Topographic Mapping (GTM) and Laplacian Eigenmaps). The considered methods were applied for the visualization of three toxicity datasets by using four sets of descriptors. Two methods, GTM and Diffusion Maps, were identified as the best approaches, which thus made it impossible to prioritize a single family of the considered dimensionality reduction methods. The intrinsic dimensionality assessment of data was performed by using the Maximum Likelihood Estimation. It was observed that descriptor sets with a higher intrinsic dimensionality contributed maps of lower quality. A new statistical coefficient, which combines two previously known ones, was proposed to automatically rank the maps. Instead of relying on one of the best methods, we propose to automatically generate maps with different parameter values for different descriptor sets. By following this procedure, the maps with the highest values of the introduced statistical coefficient can be automatically selected and used as a starting point for visual inspection by the user.