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.     

Sorption of Nitric Oxides from Exhaust Nitrous Gases Using Different Reagents (A Review)

Russian Journal of General Chemistry - The main sources of emissions of industrial nitrogen oxides have been reviewed. A promising method for the absorption of nitrogen monoxide by a reusable...     

Synthesis of Small‐Sized,Porous, and Low‐Toxic Magnetite Nanoparticles by Thin POSS Silica Coating

In this communication, we report the synthesis of small‐sized (<10 nm), water‐soluble, magnetic nanoparticles (MNPs) coated with polyhedral oligomeric silsesquioxanes (POSS), which contain either polyethylene glycol (PEG) or octa(tetramethylammonium) (OctaTMA) as functional groups. The POSS‐coated MNPs exhibit superparamagnetic behavior with saturation magnetic moments (51–53 emu g^?1) comparable to silica‐coated MNPs. They also provide good colloidal stability at different pH and salt concentrations, and low cytotoxicity to MCF‐7 human breast epithelial cells. The relaxivity data and magnetic resonance (MR) phantom images demonstrate the potential application of these MNPs in bioimaging.     

Design and investigation of UV image detectors

The results of investigation of optical image detectors designed for the largest problem, near-VUV, range of the spectrum are presented. The possibility of using a dual-stage image detection system to appreciably lower the sensitivity threshold and make computer data processing feasible is considered. The integration of a UV module into a wideband image detector is studied.