In situ observation of synthesized nanoparticles in ultra-dilute aerosols via X-ray scattering

In-air epitaxy of nanostructures (Aerotaxy) has recently emerged as a viable route for fast, large-scale production. In this study, we use small-angle X-ray scattering to perform direct in-flight characterizations of the first step of this process, i.e., the engineered formation of Au and Pt aerosol nanoparticles by spark generation in a flow of N₂ gas. This represents a particular challenge for characterization because the particle density can be extremely low in controlled production. The particles produced are examined during production at operational pressures close to atmospheric conditions and exhibit a lognormal size distribution ranging from 5–100 nm. The Au and Pt particle production and detection are compared. We observe and characterize the nanoparticles at different stages of synthesis and extract the corresponding dominant physical properties, including the average particle diameter and sphericity, as influenced by particle sintering and the presence of aggregates. We observe highly sorted and sintered spherical Au nanoparticles at ultra-dilute concentrations (< 5 × 10⁵ particles/cm³) corresponding to a volume fraction below 3 × 10^–10, which is orders of magnitude below that of previously measured aerosols. We independently confirm an average particle radius of 25 nm via Guinier and Kratky plot analysis. Our study indicates that with high-intensity synchrotron beams and careful consideration of background removal, size and shape information can be obtained for extremely low particle concentrations with industrially relevant narrow size distributions.

     

Distribution-free uncertainty quantification for kernel methods by gradient perturbations

Machine Learning - We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence,...     

Separation of Indium from Acid Sulfate-Containing Solutions by Ion Exchange with Impregnated Resins

Indium separation using ion exchange resins from acidic polymetallic and very diluted solutions are investigated. Since the selectivity of commercial ion exchange resins have proven to be too low for an effective separation from solutions with high content of other metals, Lewatit® TP 208 was impregnated with common extractants to enhance its properties. By resin impregnation with D2EHPA and Cyanex 272, not only the selective indium recovery was reached but also the resin capacity was increased approx. two times. The best loading and elution performance were shown by Cyanex 272-impregnated Lewatit® TP 208, increasing the indium purity in the eluate from 0.75 % to 85 %.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Chemistry and Toxicology of Major Bioactive Substances in Inocybe Mushrooms

Mushroom poisoning has always been a threat to human health. There are a large number of reports about ingestion of poisonous mushrooms every year around the world. It attracts the attention of researchers, especially in the aspects of toxin composition, toxic mechanism and toxin application in poisonous mushroom. Inocybe is a large genus of mushrooms and contains toxic substances including muscarine, psilocybin, psilocin, aeruginascin, lectins and baeocystin. In order to prevent and remedy mushroom poisoning, it is significant to clarify the toxic effects and mechanisms of these bioactive substances. In this review article, we summarize the chemistry, most known toxic effects and mechanisms of major toxic substances in Inocybe mushrooms, especially muscarine, psilocybin and psilocin. Their available toxicity data (different species, different administration routes) published formerly are also summarized. In addition, the treatment and medical application of these toxic substances in Inocybe mushrooms are also discussed. We hope that this review will help understanding of the chemistry and toxicology of Inocybe mushrooms as well as the potential clinical application of its bioactive substances to benefit human beings.     

Biodegradability Study of the Biosurfactant Contained in a Crude Extract from Corn Steep Water

Over the last few years, the global biosurfactant market has raised due to the increasing awareness among consumers, for the use of biological or bio-based products. Because of their composition, it can be speculated that these are more biocompatible and more biodegradable than their chemical homologous. However, at the moment, no studies exist in the literature about the biodegradability of biosurfactants. In this work, a biosurfactant contained in a crude extract, obtained from a corn wet-milling industry stream that ferments spontaneously in the presence of lactic acid bacteria, was subjected to a biodegradation study, without addition of external microbial biomass, under different conditions of temperature (5–45 °C), biodegradation time (15–55 days), and pH (5–7). For that, a Box–Behnken factorial design was applied, which allowed to predict the percentage of biodegradation for the biosurfactant contained in the crude extract, between the range of the independent variables selected in the study, obtaining biodegradation values between 3 and 80%. The percentage of biodegradation for the biosurfactant was calculated based on the increase in the surface tension of samples of the crude extract. Furthermore, it was also possible to predict the variation in t_1/2 for the biosurfactant (time to achieve the 50% of biodegradation) under different conditions.