Asian Journal of Nanoscience and Materials
Volume:1 Issue: 1, Mar 2018

A quantitative structure–retention relation (QSRR) study was conducted on the range-scaling transformation (Xa) of the nanoparticle compounds which obtained by comprehensive two-dimensional gas chromatography (GC×GC) stationary phases consisting of thin films of the gold-centered monolayer protected nanoparticles (MPNs) system. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between the selected molecular descriptors and the retention time was achieved by linear (partial least square; PLS) and nonlinear (Levenberg-Marquardt artificial neural network; L-M ANN) methods. Linear and nonlinear methods resulted in an accurate prediction whereas more accurate results were obtained by L-M ANN model.

Silica supported-boron sulfonic acid (SBSA) was used as a cheap and mild bronsted acidic in the reaction of indole with aldehydes to afford the corresponding bis(indolyl)methanesin in solvent free grinding and room temperature. The catalyst is also effective in the reaction in good yields. This methodology offers several advantages, such as good yields, reusability of catalyst, short reaction times, simple procedure, and mild conditions. The catalyst can be recovered and reused without loss of activity. The work-up of the reaction consists of a simple filtration, followed by concentration of the crude product and purification.

We describe the synthesis of silver nanoparticles (Ag-NPs) using aqueous extract of Dracocephalum lindbergii . UV–visible spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray energy dispersive spectrophotometer (EDX) were performed to ascertain the formation of Ag-NPs. UV-visible absorption spectra of the reaction medium containing silver nanoparticles showed maximum absorbance at 416 nm. The XRD pattern revealed the crystalline structure of SNPs. The SEM analysis showed the size and shape of the nanoparticles. The environmental friendly method provides simple, easy and cost effective faster synthesis of nanoparticles than chemical methods and can be used in several areas such as food, medicine.

The pesticides and active pharmaceutical compounds in water can potentially causedamage, including theincreased cancer risk; liver, and kidney. A quantitative structure–retention relationship (QSRR) was developed using the partial least square (PLS), Kernel PLS (KPLS), and Levenberg-Marquardt artificial neural network (L-M ANN) approach for chemometrics study. The data contained retention time (RT) of the 87 pesticides and active pharmaceutical compounds in wastewater and river waters. Genetic algorithm was employed as a factor selection procedure for PLS and KPLS modeling methods. The results showed that, the GA-PLS descriptors are selected for L-M ANN. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN.

Facile and green one pot hydrothermal method was used for synthesis of fluorescent carbon quantum dots (CQDs) using citrus lemon juice as precursor. The synthesized CQDs were characterized using UV–Vis spectrophotometer, fluorescence spectrometer, transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectrometer (FTIR), field emission scanning electron microscope equiped with energy dispersive X-ray spectroscopy (FESEM-EDS) and fluorescence microscopy. The obtained CQDs have high photoluminescence of 10.20% quantum yield. The photoluminescence intensity of CQDs depends on pH of the solution and maximum intensity obtained at pH of 6. The particle size of the carbon dots were distributed in narrow range of 2–10 nm with an average of 5.8 nm. The highly water soluble CQDs have high cell viability even at high concentration which rich up to 85%. MTT assay was used to investigate the potential application of CQDs and the results indicated that the material can be used as florescent probe in the cell imaging.

Let G be the connected graph with vertex set V(G) and edge set E(G).The first and second K Banhatti indices of G are defined as B1(G)=Σue[dG (u) +dG (e)] and B2(G)=Σue[dG (u) +dG (e)]  where ue means that the vertex u and edge e are incident in G.The first and second K hyper Banhatti indices of G are defined as HB1(G) = Σue[dg(u) + dG (e)]2 and HB2(G) = Σue[dg(u) dG (e)]2 respectively . In this paper, we compute the first and second K Banhatti indices of toroidal polyhex network. In addition, the first and second K hyper Banhatti indices of toroidal polyhex networks are determined. Keywords: Topological index, Banhatti index, Network.