New developments in laser interferometry at 10.6 μm

A complete setup for pulsed CO₂ laser interferometry is described. The relative advantages and limits of different recording techniques of the interference pattern in 10.6 μm laser light are analysed. Preliminary recordings of the interference pattern in the case of early evolution stages of an air breakdown plasma in front of a solid target are given.     

Investigations on the gas-phase photolysis and OH radical kinetics of methyl-2-nitrophenols

Methyl-2-nitrophenols can be emitted directly to the atmosphere or can be formed in situ via the oxidation of aromatic hydrocarbons. Nitrophenols possess phytotoxic properties and recent studies indicate their photooxidation is effective in producing secondary organic aerosols. Therefore, investigations on the major photooxidation pathways of these compounds with respect to assessing their environmental impacts and effects on human health are highly relevant. Presented here are determinations of the rate coefficients for the reactions of OH radicals with four methyl-2-nitrophenol isomers using a relative kinetic technique. The experiments were performed in a 1080 l photoreactor at (760 +/- 10) Torr total pressure of synthetic air at (296 +/- 3) K. The following rate coefficients (in units of cm(3) molecule(-1) s(-1)) have been obtained: 3-methyl-2-nitrophenol, (3.69 +/- 0.70) x 10(-12); 4-methyl-2-nitrophenol, (3.59 +/- 1.17) x 10(-12); 5-methyl-2-nitrophenol, (6.72 +/- 2.14) x 10(-12); 6-methyl-2-nitrophenol, (2.70 +/- 0.57) x 10(-12). Photolysis of the methyl-2-nitrophenols with the superactinic fluorescent lamps (320 < lambda < 480 nm, lambda(max) = 360 nm) used in the experiments was observed. Photolysis frequencies measured for the methyl-2-nitrophenols in the photoreactor have been determined and scaled to atmospheric conditions. The results suggest that photolysis rather than the reaction with OH radicals will be the dominant gas phase atmospheric loss process for methyl-2-nitrophenols.     

Investigation of chemical mechanical polishing of zinc oxide thin films

Zinc oxide has become an important material for various applications. Commercially available zinc oxide single crystals and as-grown zinc oxide thin films have high surface roughness which has detrimental effects on the growth of subsequent layers and device performance. A chemical mechanical polishing (CMP) process was developed for the polishing of zinc oxide polycrystalline thin films. Highly smooth surfaces with RMS roughness <6 Å (as compared to the initial roughness of 26 ± 6 Å) were obtained under optimized conditions with removal rates as high as 670 Å/min. Effects of various CMP parameters on removal rate and surface roughness were evaluated. The role of pH on the polishing characteristics was investigated in detail.     

Removal of uranyl ions from UO2(NO3)2 solution by means of Chlorella vulgaris and Dunaliella salina algae

This paper deals with a study of the biosorption of UO₂ ²⁺ ions on two green algae: Chlorella vulgaris and Dunaliella salina. By investigating the retention degree versus contact time from Langmuir and Freundlich biosorption isotherms, kinetic investigations and FTIR spectra it was found that the biosorption process was greater for Chlorella vulgaris than for Dunaliella salina. A new kinetics method is proposed to establish the reaction order concerning the biosorption process of uranyl ions on these biomasses.      

Schwarzschild-de-Sitter Solution in Quantum Gauge Theory of Gravity

We use the theory based on the gravitational gauge group G to obtain a spherical symmetric solution of the field equations for the gravitational potentials on a Minkowski space-time. The gauge group G is defined and then we introduce the gauge-covariant derivative Dμ. The strength tensor of the gravitational gauge field is also obtained and a gauge-invariant Lagrangian including the cosmological constant is constructed. A model whose gravitational gauge potentials A^α μ （x） have spherical symmetry, depending only on the radial coordinate τ is considered and an analytical solution of these equations, which induces the Schwarzschild-de-Sitter metric on the gauge group space, is then determined. All the calculations have been performed by GR Tensor II computer algebra package, running on the Maple V platform, along with several routines that we have written for our model.     

Kinetic–catalytic–spectrophotometric determination of low concentrations of molybdenum in white wines

The paper describes the determination of the molybdenum content in white wines based on its catalytical action on the kalium iodide oxidation by hydrogen peroxide in acid medium.

The optimum reaction conditions (the catalyst, KI and H₂O₂ concentrations, the pH value, the order of the reagent additions, the temperature) have been found by studying the effect of the reaction variables.

The influence of some metallic ions (Ca²⁺, Mg²⁺, Zn²⁺, Cd²⁺, Fe²⁺ and Fe³⁺) and complexing anions (F⁻, C₂O²⁻₄, EDTA⁴⁻) on the catalyzed reaction rate was elucidated.

The molybdenum concentration was estimated by the tangent, fixed-time and fixed-absorbance method. The obtained average values for molybdenum content in white wines are within the 1.77×10⁻⁷–1.83×10⁻⁷ mol l⁻¹ range.     

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.