Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

Unsymmetrical dimethylhydrazine (UDMH) is a widely used rocket propellant. Entering the environment or being stored in uncontrolled conditions, UDMH easily forms an enormous variety (at least many dozens) of transformation products. Environmental pollution by UDMH and its transformation products is a major problem in many countries and across the Arctic region. Unfortunately, previous works often use only electron ionization mass spectrometry with a library search, or they consider only the molecular formula to propose the structures of new products. This is quite an unreliable approach. It was demonstrated that a newly proposed artificial intelligence-based workflow allows for the proposal of structures of UDMH transformation products with a greater degree of certainty. The presented free and open-source software with a convenient graphical user interface facilitates the non-target analysis of industrial samples. It has bundled machine learning models for the prediction of retention indices and mass spectra. A critical analysis of whether a combination of several methods of chromatography and mass spectrometry allows us to elucidate the structure of an unknown UDMH transformation product was provided. It was demonstrated that the use of gas chromatographic retention indices for two stationary phases (polar and non-polar) allows for the rejection of false candidates in many cases when only one retention index is not enough. The structures of five previously unknown UDMH transformation products were proposed, and four previously proposed structures were refined.

Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.

Chromatography–mass spectrometry and MALDI mass spectrometry have been used to study the interaction of 1,1-dimethylhydrazine and products of its oxidation with the surface of mineral schungite in...

Coatings with thickness 40 to 150 μm were formed by plasma electrolytic oxidation (PEO) on the zirconium alloy Zr-1Nb (Zr-1% Nb) in the slurry electrolyte containing 9 g/L Na₂SiO₃ 5H₂O, 5 g/L Na(PH₂O₂) and 6 g/L submicron Y₂O₃ yttria powder during 60 min under the AC electrical mode at current densities 20; 30 and 40 A/dm². The surface morphology, structure, composition, and corrosion-protective ability of the formed coatings have been analyzed. At PEO current density 30 A/dm², a predominantly tetragonal phase of zirconia was formed in coatings. Increasing the PEO current density up to 40 A/dm² promoted the formation of the coating surface layer containing submicron yttria particles. Electrochemical polarization studies in 0.5% LiOH solution showed that PEO coatings demonstrated high corrosion-protective ability. The dependence of the polarization currents on the PEO current density was found to be inconsequential.

Ionized products of desorption of biologically active amino compounds (1,1-dimethylhydrazine, diglycine, tyrosine, and tryptophan) from the surface of a modified mineral (shungite-III, Karelia) have been investigated by laser desorption/ionization mass spectrometry methods (MALDI and SALDI). The composition of the ionized products of laser desorption of amino acids from the surface of nanodispersed shungite is almost independent of carbon content in shungite. The comparative analysis of ionized organic compounds desorbed from the surfaces of shungite and carbon black has led to the suggestion that the decomposition and surface transformation of 1,1-dimethylhydrazine molecules mainly occur on the inorganic components of shungite.