Electron Energy-Loss Spectroscopy Study of the Change in the Free-Electron Density in Thin Superconducting NbN Films under Ion-Beam Irradiation

The change in the free-electron density in ultrathin (5 nm) superconducting NbN films in the initial state and after irradiation by O⁺ ions to doses of (0.1–0.9) × 10¹⁷ cm^–2 has been studied by electron energy-loss spectroscopy (EELS). The analysis has been performed on cross section samples prepared by the focused ion beam method, using plasmon oscillations with energies up to 50 eV. The radiation-induced replacement of nitrogen atoms with oxygen atoms in niobium nitride is found to change the electrical properties of the material, which leads to a decrease in the free-electron density with an increase in the irradiation dose.     

Biodistribution study of free and microencapsulated 6‐methylcoumarin in Wistar rats by HPLC

A sensitive, specific and reproducible HPLC method has been developed and validated for the quantitative determination of 6‐methylcoumarin (6MC) in plasma and other tissues in Wistar rats. A C₁₈ column was used with UV detection at 321 nm and a gradient system consisting of methanol‐deionized water was used as mobile phase. The retention time for 6MC was 14.921 min and no interfering peaks were observed for any of the matrices. Linear relationships (r² > 0.997) were obtained between the peak height ratios and the corresponding biological sample concentrations over the range 0.4–12.8 µg/mL. Precision and accuracy were evaluated; the coefficient of variation and the relative error for all of the organs were <2 and 7%, respectively. The limit of quantitation was 0.20 µg/mL for the heart and 0.30 µg/mL for the other tissues evaluated. This HPLC method was successfully used in the determination of 6MC in the biodistribution study after administration of 200 mg/kg of both 6MC‐free and 6MC‐loaded polymeric microparticles. In this study, extensive 6MC was found, in both free and microencapsulated forms, in all the organs tested. The 6MC‐free showed a range of between 1.7 and 11.5 µg/g, while the microencapsulated 6MC showed concentrations of between 6.35 and 17.7 µg/g, suggesting that 6MC improved absorption rate. Copyright © 2014 John Wiley & Sons, Ltd.     

Experimental and Theoretical Studies on the Rearrangement of 2‐Oxoazepane α,α‐Amino Acids into 2′‐Oxopiperidine β2,3,3‐Amino Acids: An Example of Intramolecular Catalysis

Enantiopure β‐amino acids represent interesting scaffolds for peptidomimetics, foldamers and bioactive compounds. However, the synthesis of highly substituted analogues is still a major challenge. Herein, we describe the spontaneous rearrangement of 4‐carboxy‐2‐oxoazepane α,α‐amino acids to lead to 2′‐oxopiperidine‐containing β^2,3,3‐amino acids, upon basic or acid hydrolysis of the 2‐oxoazepane α,α‐amino acid ester. Under acidic conditions, a totally stereoselective synthetic route has been developed. The reordering process involved the spontaneous breakdown of an amide bond, which typically requires strong conditions, and the formation of a new bond leading to the six‐membered heterocycle. A quantum mechanical study was carried out to obtain insight into the remarkable ease of this rearrangement, which occurs at room temperature, either in solution or upon storage of the 4‐carboxylic acid substituted 2‐oxoazepane derivatives. This theoretical study suggests that the rearrangement process occurs through a concerted mechanism, in which the energy of the transition states can be lowered by the participation of a catalytic water molecule. Interestingly, it also suggested a role for the carboxylic acid at position 4 of the 2‐oxoazepane ring, which facilitates this rearrangement, participating directly in the intramolecular catalysis.     

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.