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.

Here, low-dimensional mixed metal oxide (ZnO/NiO/MnO₂) nanoparticles (NPs) were prepared to develop a selective, efficient and ultra-sensitive 1,4-dioxane sensor by using the wet-chemical method (co-precipitation) in alkaline medium at low temperature. Detailed characterization of the prepared calcined NPs was achieved <i>via</i> conventional methods, including X-ray diffraction, field emission scanning electron microscopy, and X-ray photoelectron, UV-vis, Fourier-transform infrared and energy dispersive X-ray spectroscopies. To develop a thin layer of nanomaterial on the fabricated electrode, a slurry of prepared NPs was used to coat the glassy carbon electrode (GCE) with conductive Nafion (5% in ethanol) binder. The fabricated electrochemical sensor showed good sensitivity (1.0417 μA μM^-1 cm^-2), a wide linear dynamic range (0.12 nM to 1.2 mM), lower detection limit (9.14 ± 4.55 pM), short response time, good reproducibility, and long-term stability to selectively detect 1,4-dioxane in the optimized buffer system. Thus, this work presents a reliable alternative approach over existing methods to selectively detect hazardous chemicals in large scale for safety in the environmental and healthcare fields.

Advanced nano composites having light weight, highly thermally stable and easily processable are the demand of modern technology era. The problem keeping in mind, an advanced material, organic-inorganic nano-composite cation-exchanger, for preparation, physico-chemical characterization and their applications has been discussed as compilation work in the form of a book. We discussed some polymeric-inorganic cation exchanger having metal with phosphate group. The main objective of our proposed work was to synthesize advance class organic-inorganic hybrid materials and make it suitable to apply in electronics and analytical techniques.

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.

The NP-hard problem of determining the number of spanning trees of graphs is examined in this paper. A spanning tree of a graph is a tree such that (i) it is a subgraph of (i.e., that includes only edges from ), and (ii) it includes every vertex of . The most classical interest concerning a spanning tree is the number of spanning trees or the complexity of the graph denoted by We propose the first attempt to use a binary version of the fruit fly optimization algorithm (BFFOA) to compute the minimal spanning tree of a graph in a heuristic manner. The fruit fly of BFFOA is binary encoded and used to represent which one of the vertices of the graph belongs to the spanning tree of . The feasibility is enforced by repairing the fruit fly such that an extra vertex created from vertices of is added to , and this repairing process is repeated until becomes the spanning tree of . Theoretically computed graph results are used to compare the proposed BFFOA against competing techniques. The proposed BFFOA performs better than the binary Grey Wolf Optimizer (BGWO), the binary Particle Swarm Optimizer (BPSO), the binary Whale Optimizer (BWO), and the binary Multi-verse Optimization (BMVO) algorithms, according to analysis and computational results.