Electrochemical behaviour of zinc in alkaline solutions

As the whole passivation phenomenon in the case of zinc is very quick and sudden and as it cannot be fully studied and followed by galvanostatic techniques alone, the constant over-potential technique has, for the first time, been applied to zinc and results reported. A special apparatus consisting of suitable oscillator, modulator, demodulator and a stable D.C. amplifier with a gain of 100,000 was used for the purposes and is described. Potential curves for equilibrium current rates, achieved on 0·1 V. and 25 mV. steps after 1 minute each, have been obtained in 6N, N, 0·1 N KOH and zincate solutions for the complete range of ?1·3 to about ?2·0 volts with reference to Hg/HgO/KOH reference electrode. It has been found that in the first truly active region, the main electrode reaction is the formation of zinc ions while after the passivation it changes to gas evolution. Potentiostatic techniques reveal intermediary stages, undisclosed by constant current methods, of pseudo-passivation and current-plateau regions in which the anodic layer thickens, controlled by the high field cation transport. These observations and explanations are further supported by plotting rate-time transients obtained by suddenly dropping the potentials from higher to lower values, when the rates were found to cut off. Some anomalies and sudden reversal of currents with increasing over-voltages, have also been fully discussed. The influence of other factors,e.g., concentration, stirring, sudden changes in over-voltages, presence of zincate, sulphate, etc., has also been considered. Studies such as these are found to throw considerable light on the electrochemical behaviour of zinc.     

Crystal structure and physical characterization of neotame methanol solvate

The crystal structure of the methanol solvate (empirical formula: 2C₂₀H₃₀N₂O₅·3CH₃OH) of a new dipeptide sweetener, neotame (N-(3,3-dimethylbutyl)-L--aspartyl-L-phenylalanine 1-methyl ester), has been determined. Crystal data: a = 9.8989(1), b = 18.1331(1), c = 27.5725(1) Å, orthorhombic, space group P2₁2₁2₁, with Z = 4. Each unit cell includes 8 neotame and 12 methanol molecules. Disorder exists in one neotame molecule and one methanol molecule. The crystals were characterized by the following techniques: hot-stage microscopy (HSM), Karl-Fischer titrimetry (KFT), powder X-ray diffractometry (PXRD), differential scanning calorimetry (DSC), thermogravimetry (TGA), ¹³C solid-state nuclear magnetic resonance (SSNMR) spectroscopy. Under HSM at a heating rate of 10°C/min in silicone oil, the sample melts at 64–84°C and liberates bubbles at 71–86°C. DSC in open pans shows two overlapping endotherms at 56 and 71°C, probably due to melting and desolvation, respectively. TGA in open pans shows 5.9% weight loss due to desolvation below 70°C. Under house vacuum (23 mm Hg) over phosphorus pentoxide at 23°C, the methanol solvate produces pure amorphous anhydrate, which converts to crystalline neotame monohydrate in the presence of moisture.     

Development of Carbon Materials for Energy and Environmental Applications

Methodologies for generating carbon materials from unusual natural sources like Limonea acidissima and Calotropis gigantea are reported. The carbon materials thus obtained have been characterized using a variety of tools. The possibility of modulating the textural properties of these materials has been examined. The carbon materials synthesized have been exploited for specific applications, such as support for noble metals for use in Direct Methanol Fuel Cells and as support for heteropoly acid for the production of gasoline additive. In addition, the utility of some of the activated carbon materials (from commercial sources) for the removal of sulphur from crude petroleum sources is also examined.     

An In Silico investigation for acyclovir and its derivatives to fight the COVID-19: Molecular docking,DFT calculations,ADME and td-Molecular dynamics simulations

In the present work, we have designed three molecules, acyclovir (A), ganciclovir (G) and derivative of hydroxymethyl derivative of ganciclovir (CH₂OH of G, that is D) and investigated their biological potential against the Mpro of nCoV via in silico studies. Further, density functional theory (DFT) calculations of A, G and D were performed using Gaussian 16 on applying B3LYP under default condition to collect the information for the delocalization of electron density in their optimized geometry. Authors have also calculated various energies including free energy of A, G and D in Hartree per particle. It can be seen that D has the least free energy. As mentioned, the molecular docking of the A, G and D against the Mpro of nCoV was performed using iGemdock, an acceptable computational tool and the interaction has been studied in the form of physical data, that is, binding energy for A, G and D were calculated in kcal/mol. It can be seen the D showed effective binding, that is, maximum inhibition that A and G. For a better understanding for the inhibition of the Mpro of nCoV by A, G and D, temperature dependent molecular dynamics simulations were performed. Different trajectories like RMSD, RMSF, Rg and hydrogen bond were extracted and analyzed. The results of molecular docking of A, G and D corroborate with the td-MD simulations and hypothesized that D could be a promising candidate to inhibit the activity of Mpro of nCoV.     

Medium energy,heavy and inert ion irradiation of metallic thin films: studies of surface nano‐structuring and metal burrowing

In context to the ion induced surface nanostructuring of metals and their burrowing in the substrates, we report the influence of Xe and Kr ion‐irradiation on Pt:Si and Ag:Si thin films of ~5‐nm thickness. For the irradiation of thin films, several ion energies (275 and 350 keV of Kr; 450 and 700 keV of Xe) were chosen to maintain a constant ratio of the nuclear energy loss to the electronic energy loss (S_n/S_e) in Pt and Ag films (five in present studies). The ion‐fluence was varied from 1.0 × 10¹⁵ to 1.0 × 10¹⁷ ions/cm². The irradiated films were characterized using Rutherford backscattering spectroscopy (RBS), atomic force microscopy (AFM) and scanning electron microscopy (SEM). The AFM and SEM images show ion beam induced systematic surface nano‐structuring of thin films. The surface nano‐structures evolve with the ion fluence. The RBS spectra show fluence dependent burrowing of Pt and Ag in Si upon the irradiation of both ion beams. At highest fluence, the depth of metal burrowing in Si for all irradiation conditions remains almost constant confirming the synergistic effect of energy losses by the ion beams. The RBS analysis also shows quite large sputtering of thin films bombarded with ion beams. The sputtering yield varied from 54% to 62% by irradiating the thin films with Xe and Kr ions of chosen energies at highest ion fluence. In the paper, we present the experimental results and discuss the ion induced surface nano‐structuring of Pt and Ag and their burrowing in Si. Copyright © 2016 John Wiley & Sons, Ltd.     

Phosphorus-containing imide resins. III

Three imide monomers were prepared by reacting maleic anhydride; 2,2-bis(3,4-dicarboxyphenyl) hexafluoropropane dianhydride, bis(m-aminophenyl) methyl phosphine oxide, and tris(m-aminophenyl) methyl phosphine oxide, and tris (m-aminophenyl) phosphine oxide. The characterization was done by elemental analysis and infrared spectroscopy (IR). These monomers were soluble in acetone. Their thermal polymerization was investigated by differential scanning calorimetry (DSC). The temperature of the exothermic peak position was influenced by the presence of free amino group in the monomer and was about 50°C higher in monomers that did not contain amino group. Anaerobic char yield of imide monomers cured at 270 ± 2°C for 30 min ranged from 58 to 64%. Graphite cloth laminate fabricated from one of these resins had a limiting oxygen index of 100.     

Kernel logistic regression using truncated Newton method

Kernel logistic regression (KLR) is a powerful nonlinear classifier. The combination of KLR and the truncated-regularized iteratively re-weighted least-squares (TR-IRLS) algorithm, has led to a powerful classification method using small-to-medium size data sets. This method (algorithm), is called truncated-regularized kernel logistic regression (TR-KLR). Compared to support vector machines (SVM) and TR-IRLS on twelve benchmark publicly available data sets, the proposed TR-KLR algorithm is as accurate as, and much faster than, SVM and more accurate than TR-IRLS. The TR-KLR algorithm also has the advantage of providing direct prediction probabilities.     

Analyzing coordination preferences of Mg2+ complexes: insights from computational and database study

Solvation of metal cations has attracted substantial interest on account of its functional importance in biological systems. In the present study, we undertake a comprehensive analysis of hydrated complexes of Mg²⁺ with up to 20 water molecules using MP2/cc-pVTZ and density functional theory (DFT) calculations. The effect of first, second, and higher solvation shells on magnesium coordination has been systematically analyzed by considering Mg²⁺(H₂O) _n complexes. Numerous competing conformations for each of the metal ion complexes have been explored and the minima structures obtained were further analyzed. The study probes the relative preferences among various coordination numbers and unambiguously establishes that coordination number 6 is the most optimal choice. The relative abundance of Mg²⁺ ion and its coordination with water and other ligands has been analyzed in the Protein Data Bank and Cambridge Structural Database. It is noted that the M–O distance and charge transfer to metal ion increase as the number of solvating water molecules increases. The computational studies are in excellent agreement with the experimental observations, and provide support to multiple coordinate site preferences for Mg²⁺.     

Two minor diterpene glycosides from the leaves of Stevia rebaudiana

Two new new diterpene glycosides, 13-[(2-O-(6-O-beta-D-glucopyranosyl)-beta-D-glucopyranosyl-beta-D-glucopyranosyl)oxy] kaur-16-en-18-oic acid beta-D-glucopyranosyl ester (1) and 13-[(2-O-beta-D-glucopyranosyl-3-O-beta-D-fructofuranosyl-beta-D-glucopyranosyl)oxy] kaur-16-en-18-oic acid beta-D-glucopyranosyl ester (2) were isolated from the leaves of Stevia rebaudiana, along with the known steviol glycosides stevioside, rebaudiosides A-F and dulcoside A. The structures of the two new compounds were established on the basis of extensive 2D NMR (COSY, HSQC, and HMBC), MS and chemical studies.     

A novel content-based active contour model for brain tumor segmentation

Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based active contour models such as gradient vector flow (GVF), magneto static active contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based active contour (CBAC) uses both intensity and texture information present within the active contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients - more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation.