Production of ammonia makes Venusian clouds habitable and explains observed cloud-level chemical anomalies

The atmosphere of Venus remains mysterious, with many outstanding chemical connundra. These include the unexpected presence of ∼10 ppm O2 in the cloud layers, an unknown composition of large particles in the lower cloud layers, and hard to explain measured vertical abundance profiles of SO2 and H2O. We propose a hypothesis for the chemistry in the clouds that largely addresses all of the above anomalies. We include ammonia (NH3), a key component that has been tentatively detected both by the Venera 8 and Pioneer Venus probes. NH3 dissolves in some of the sulfuric acid cloud droplets, effectively neutralizing the acid and trapping dissolved SO2 as ammonium sulfite salts. This trapping of SO2 in the clouds, together with the release of SO2 below the clouds as the droplets settle out to higher temperatures, explains the vertical SO2 abundance anomaly. A consequence of the presence of NH3 is that some Venus cloud droplets must be semisolid ammonium salt slurries, with a pH of ∼1, which matches Earth acidophile environments, rather than concentrated sulfuric acid. The source of NH3 is unknown but could involve biological production; if so, then the most energy-efficient NH3-producing reaction also creates O2, explaining the detection of O2 in the cloud layers. Our model therefore predicts that the clouds are more habitable than previously thought, and may be inhabited. Unlike prior atmospheric models, ours does not require forced chemical constraints to match the data. Our hypothesis, guided by existing observations, can be tested by new Venus in situ measurements.

Assessment of Wall Salinity in the Selection of Renovation Plaster Systems

This article presents issues related to assessing the degree of wall salinity to select plaster systems for renovations of damp building walls. The most commonly used salt concentration tests pose many difficulties. If used uncritically, they risk failing to select the right system or its incorrect make. The accuracy of the colorimetric method for testing chloride, nitrate, and sulphate content was analysed to exemplify the magnitude of the problem. Both multi-salt solutions of known concentrations and unknown composition extracted from drillings in the walls of a historical facility were examined. A comparative methodology using ion chromatography as a standard was employed in the research. The analytical methods and the selected modules of the „Statistica” software were used to analyse data and present the results. The colorimetric method has been shown to distort salt concentration values, posing a risk of unsuccessful repair work on high-salinity walls. A method for determining the correction reducing the measurement error has been proposed. The factors affecting the error were also mentioned. Attention has also been drawn to the resolution and application of a method with a correct concentration range intended to improve work efficiency and optimize the costs incurred in renovating the salty wall.

Assignment-free chirality detection in unknown samples via microwave three-wave mixing

Straightforward identification of chiral molecules in multi-component mixtures of unknown composition is extremely challenging. Current spectrometric and chromatographic methods cannot unambiguously identify components while the state of the art spectroscopic methods are limited by the difficult and time-consuming task of spectral assignment. Here, we introduce a highly sensitive generalized version of microwave three-wave mixing that uses broad-spectrum fields to detect chiral molecules in enantiomeric excess without any prior chemical knowledge of the sample. This method does not require spectral assignment as a necessary step to extract information out of a spectrum. We demonstrate our method by recording three-wave mixing spectra of multi-component samples that provide direct evidence of enantiomeric excess. Our method opens up new capabilities in ultrasensitive phase-coherent spectroscopic detection that can be applied for chiral detection in real-life mixtures, raw products of chemical reactions and difficult to assign novel exotic species.

Estimating Activity Coefficients of Target Components in Poorly Specified Mixtures with NMR Spectroscopy and COSMO-RS

Poorly specified mixtures are common in process engineering, especially in bioprocess engineering. The properties of such mixtures of unknown composition cannot be described using conventional thermodynamic models. The NEAT method, which has recently been developed in our group, enables the calculation of activity coefficients of known target components in such poorly specified mixtures. In NEAT, the group composition of the mixture is determined by NMR spectroscopy and a thermodynamic group contribution method is used for calculating the activity coefficients. In all previous studies with NEAT, the UNIFAC group contribution method was used. In the present work, we demonstrate that NEAT can also be applied with another important method for predicting activity coefficients: COSMO-RS. COSMO-RS (OL) developed in Oldenburg together with its group contribution version GC-COSMO-RS (OL) is used here. The new version of NEAT was successfully tested. For a variety of aqueous mixtures excellent agreement of the NEAT predictions, for which only information on the target component was used, with results that were obtained using the full knowledge on the composition of the mixture was found. The results demonstrate the generic nature of the idea of NEAT and the broad applicability of the method.

Effects of composition and storage duration of mechanically deboned poultry meat on sensory properties of frankfurters

This research aimed to study the influence of differences in the composition and storage length of mechanically deboned poultry meat (MDPM) on the sensory properties of frankfurters. Three variants of frankfurters were produced from three respective alternatives of MDPM that differed solely in proportions of meat from broiler backs and necks. Similarly, a commercially available and freshly produced MDPM of unknown composition was used as the control. All the four variants of MDPM were stored at -18 °C for 1, 45 and 90 days. Sensory profiling of the frankfurters was performed by 8 panellists using a quantitative-descriptive analysis (QDA). Two-factorial ANOVA and principal component analysis (PCA) of the sensory evaluation results revealed significant (p < 0.05) effects of the storage time of the MDPM variants on sensory characteristics of the frankfurters, regardless of their composition.

On the question of the significance of tissue decay products. Experiments with the prostate gland

At present, treatment with animal drugs (opotherapy) has become widespread. There are hundreds of such drugs under a wide variety of names and sometimes with a completely unknown composition.

A new strategy for the computer-assisted development of reversed-phase liquid chromatography separation methods of unknown sample mixtures

A new strategy for the computer-assisted methods development in the reversed-phase liquid chromatographic separations of unknown sample mixtures has been developed using the latent spectral information in chromatogram raw data files of appropriately designed experiments, rather than resorting to elemental information functions (e.g., the number of peaks in chromatograms or similar criteria). The strategy developed allows unification of the approach for samples of both known and unknown composition and, thus, provide a general strategy for computer-aided tools in the chromatography laboratory. The operation principle of this strategy departs from extracting the spectra of components in the mixture chromatograms by resorting to multivariate curve resolution-alternating least squares (MCR-ALS). This technique allows the estimation of the true spectra for the individual components except when they have identical spectra or are fully overlapped. Thus, a convenient experimental design will try to perform separations of the sample mixture having at least partial resolution of components in some runs. This will allow estimating the spectra of components and, then, assign these components to the peaks in each run chromatogram. In this way, a retention model can be built for each component so computerized optimization process can be developed to provide the chromatographer with the best possible separation programs. Following this approach, strategies for sample mixtures of known and unknown composition are only different in the need of an initial spectrum discovery process for unknown mixtures and therefore a real general approach for the computer-assisted LC methods development is now available for the first time.

Supervised topic modeling for predicting molecular substructure from mass spectrometry

Small-molecule metabolites are principal actors in myriad phenomena across biochemistry and serve as an important source of biomarkers and drug candidates. Given a sample of unknown composition, identifying the metabolites present is difficult given the large number of small molecules both known and yet to be discovered. Even for biofluids such as human blood, building reliable ways of identifying biomarkers is challenging. A workhorse method for characterizing individual molecules in such untargeted metabolomics studies is tandem mass spectrometry (MS/MS). MS/MS spectra provide rich information about chemical composition. However, structural characterization from spectra corresponding to unknown molecules remains a bottleneck in metabolomics. Current methods often rely on matching to pre-existing databases in one form or another. Here we develop a preprocessing scheme and supervised topic modeling approach to identify modular groups of spectrum fragments and neutral losses corresponding to chemical substructures using labeled latent Dirichlet allocation (LLDA) to map spectrum features to known chemical structures. These structures appear in new unknown spectra and can be predicted. We find that LLDA is an interpretable and reliable method for structure prediction from MS/MS spectra. Specifically, the LLDA approach has the following advantages: (a) molecular topics are interpretable; (b) A practitioner can select any set of chemical structure labels relevant to their problem; (c ) LLDA performs well and can exceed the performance of other methods in predicting substructures in novel contexts.