Selected ice nanoparticle accelerator hypervelocity impact mass spectrometer (SELINA-HIMS): features and impacts of charged particles.

The selected ice nanoparticle accelerator, SELINA, was used to prepare beams of single ice particles with positive or negative charge. Positively charged particles were prepared from deionized water and 0.05-0.2 molar solutions of sodium chloride in water, and negatively charged ice particles were generated from water without salt. Depending on the electrospray source configuration, the measured particles vary from 50 to 1000 nm in diameter. The kinetic energy per charge for all particles was set to 200 eV by the collisional equilibration in quadrupoles, which resulted in primary velocities up to 600 m/s for the lowest m/z particles. The electrospray ionization and thus particle formation from SELINA become less efficient with increasing salt concentration, resulting in a lower detected particle frequency and size. Good instrument operation is achievable for concentrations below 0.2 M. After we have verified and characterized positively and negatively charged ice particles, we have combined SELINA with a target and time-of-flight spectrometer for a 'proof-of-principle' post acceleration of 120 nm particles towards hypervelocity (v ~ 3000 m/s) and detection of fragments from the particle impact (SELINA-HIMS). General conditions are discussed for the acceleration of particles between 50 and 1000 nm to velocities well above 3000 m/s with SELINA-HIMS instrument. This article is part of the theme issue 'Dust in the Solar System and beyond'.

Natural Occurrence of Nitrite-Related Compounds in Malt and Beer.

Despite sufficient control of volatile N-nitrosamines in foods and beverages, little attention remained on nonvolatile nitroso compounds, which are mostly unknown and relative to nitrite reactions. In a recent study, new compounds related to reactions of nitrite in beer were pyruvic acid oxime, 4-nitrosophenol, 4-cyanophenol, N-nitrosoproline ethyl ester, nitrosoguaiacol, and 2-methoxy-5-nitrophenol, as well as the already known N-nitrosoproline. The present study is intended to observe their natural occurrence in commercial beers and malts. All 22 nitrite-related products (N-products) were monitored in almost 200 samples of beers and malts. As many as 17 N-products were detected in malts, and all 22 N-products were found in beers. The hierarchical clustering grouped samples based on similarities of detected N-products by frequency of their appearance and level of response. Between N-products and N-nitrosodimethylamine concentrations in malts, only moderate Pearson correlations were found. The same applied to N-product correlations with the apparent total nitroso compound determination in beers.

Characterization of Nitrite-Related Reaction Products in Beer.

The effect of nitrites in foods and beverages still raises discussion due to the possible formation of harmful nitroso compounds. However, as most of these compounds in beer were not structurally characterized yet, the research about their toxicological relevance for consumers is limited. This study is focused on identification of the products formed by nitrite (or isotopically labeled nitrite 15N) reactions in beer using gas chromatography with tandem mass spectrometry. In total, 19 products were identified, and some of them were structurally characterized and confirmed by comparing retention indices and mass spectra of standard/synthesized compounds. Identified compounds were representatives of nitroso, nitro, oxime, and even cyano compounds. For the peaks which were not structurally identified, primary structural characteristics were also listed. Found products were further screened in 16 authentic beer samples which showed the apparent occurrence of found compounds in non-treated beers.

DeepReI: Deep learning-based gas chromatographic retention index predictor.

Retention index in gas chromatographic analyses is an essential tool for appropriate analyte identification. Currently, many libraries providing retention indices for a huge number of compounds on distinct stationary phase chemistries are available. However, situation could be complicated in the case of unknown unknowns not present in such libraries. The importance of identification of these compounds have risen together with a rapidly expanding interest in non-targeted analyses in the last decade. Therefore, precise in silico computation/prediction of retention indices based on a suggested molecular structure will be highly appreciated in such situations. On this basis, a predictive model based on deep learning was developed and presented in this paper. It is designed for user-friendly and accurate prediction of retention indices of compounds in gas chromatography with the semi-standard non-polar stationary phase. Simplified Molecular Input Entry System (SMILES) is used as the model's input. Architecture of the model consists of 2D-convolutional layers, together with batch normalization, max pooling, dropout, and three residual connections. The model reaches median absolute error of prediction of the retention index for validation and test set at 16.4 and 16.0 units, respectively. Median percentage error is lower than or equal to 0.81% in the case of all mentioned data sets. Finally, the DeepReI model is presented in R package, and is available on https://github.com/TomasVrzal/DeepReI together with a user-friendly graphical user interface.

The relationship between microstructure and photocatalytic behavior in lanthanum-modified 2D TiO2 nanosheets upon annealing of a freeze-cast precursor.

Titanium dioxide modified with 3 wt% La was prepared via a green freeze-casting method, and its photocatalytic activity was tested in terms of its ability to degrade 4-chlorophenol (4-CP) and remove total organic carbon (TOC). Under annealing conditions, the freeze-cast precursor was transformed into an La-modified anatase with a well-defined 2D TiO2 nanosheet morphology. Rietveld refinement of the X-ray diffraction patterns confirmed the substitutional nature of the La cation that induced local structural variations and involved subtle ion displacement in the TiO2 lattice due to the ionic size effect. Despite nearly identical tetragonal structures, replacement of Ti with La alters the photocatalytic activity through a reduction in band gap energies and an increase in charge carrier mobility. Material annealed at 650 °C exhibited the highest photocatalytic performance and achieved efficient TOC removal. Upon annealing at 800 °C, nanoscale lanthanum-enriched regions were generated due to the diffusive migration of La cations and phase transition from anatase to rutile. The La3+ cation, acting as a structural promoter, supported 2D TiO2 growth with well controlled crystallite size, surface area and porosity. La3+ could be regarded as a potential electronic promoter that can reduce the band gap of 2D TiO2 nanosheets and can provide a signature of the electron transfer and carrier charge separation. Both methods, kinetics of degradation of 4-CP and TOC, provided similar results, revealing that the photocatalytic activity under UV light irradiation increased in the order 950C < 500 °C < 800 °C < 650 °C < TiO2-P25.