Numerische Simulationen der katalytischen Methanisierung von CO2 in einem pseudo‐homogenen Strömungsrohr

Power to gas is an attractive option for storing excess energy from fluctuating renewable energy sources. In recent years, the concept has gained great interest. An essential part of the process chain of power to gas is the methanation of CO₂. Within this work the catalytic methanation of pure CO₂ and of biogas is modeled in a three‐dimensional polytropic pseudo‐homogeneous flow tube and numerically simulated at different loads. The results represent axial and radial quantitative information about the reaction behavior under the different boundary conditions.     

Textural,Rheological and Sensory Properties and Oxidative Stability of Nut Spreads—A Review

Tree nuts are rich in macro and micronutrients, phytochemicals, tocopherols and phenolic compounds. The development of nut spreads would potentially increase the food uses of nuts and introduce consumers with a healthier, non-animal breakfast snack food. Nut spreads are spreadable products made from nuts that are ground into paste. Roasting and milling (particle size reduction) are two important stages for the production of nut spreads that affected the textural, rheological characteristic and overall quality of the nut spread. Textural, color, and flavor properties of nut spreads play a major role in consumer appeal, buying decisions and eventual consumption. Stability of nut spreads is influenced by its particle size. Proper combination of ingredients (nut paste, sweetener, vegetable oil and protein sources) is also required to ensure a stable nut spread product is produced. Most of the nut spreads behaved like a non-Newtonian pseudo-plastic fluid under yield stress which help the producers how to start pumping and stirring of the nut spreads. Similar to other high oil content products, nut spreads are susceptible to autoxidation. Their oxidation can be controlled by application of antioxidants, using processing techniques that minimize tocopherol and other natural antioxidant losses.     

Polyunsaturated Fatty Acid-Enriched Lipid Fingerprint of Glioblastoma Proliferative Regions Is Differentially Regulated According to Glioblastoma Molecular Subtype

Glioblastoma (GBM) represents one of the deadliest tumors owing to a lack of effective treatments. The adverse outcomes are worsened by high rates of treatment discontinuation, caused by the severe side effects of temozolomide (TMZ), the reference treatment. Therefore, understanding TMZ’s effects on GBM and healthy brain tissue could reveal new approaches to address chemotherapy side effects. In this context, we have previously demonstrated the membrane lipidome is highly cell type-specific and very sensitive to pathophysiological states. However, little remains known as to how membrane lipids participate in GBM onset and progression. Hence, we employed an ex vivo model to assess the impact of TMZ treatment on healthy and GBM lipidome, which was established through imaging mass spectrometry techniques. This approach revealed that bioactive lipid metabolic hubs (phosphatidylinositol and phosphatidylethanolamine plasmalogen species) were altered in healthy brain tissue treated with TMZ. To better understand these changes, we interrogated RNA expression and DNA methylation datasets of the Cancer Genome Atlas database. The results enabled GBM subtypes and patient survival to be linked with the expression of enzymes accounting for the observed lipidome, thus proving that exploring the lipid changes could reveal promising therapeutic approaches for GBM, and ways to ameliorate TMZ side effects.     

An integrated supervised learning solution for monitoring process mean vector

When an out-of-control condition is detected by a control chart, a search begins to identify and eliminate the source(s) of the signal. Identification of the time when a process first changed is an important step in root cause analysis which helps a process engineer to eliminate the source(s) of assignable cause effectively. The time when a change takes place in the process is referred to as the change point. In multivariate environment, since there is more than one variable involved, then root cause analysis is relatively harder compared to the case of univariate because it is not clear exactly which variable has contributed to the out-of-control condition and in what direction its mean has shifted. Hence, a procedure that identifies the change point, performs diagnostic analysis, and specifies the direction of the shift in the mean of the contributing variable(s) all simultaneously could help to conduct root cause analysis effectively. Although different multivariate methods exist in the literature that allow to either estimate change point in the process mean vector or identify the contributing variables leading to the out-of-control condition, but in this research, an integrated supervised learning solution is proposed, which helps to (1) detect of an out-of-control condition, (2) identify the change point leading to shift in the mean vector, (3) specify the variable(s) contributing to the out-of-condition, and (4) identify the direction of the shift in the mean of each contributing variable simultaneously. A real case study is used to evaluate and compare the performance of the proposed integrated approach to existing methods in the literature.     

Simultaneous polarographic determination of isoniazid and rifampicin by differential pulse polarography method and support vector regression

A differential pulse polarography (DPP) for the simultaneous determination of isoniazid and rifampicin was proposed. Under optimum experimental conditions (pH = 7, scan rate = 10 mV/s, pulse amplitude = −50 mV), serious overlapping polarographic peaks were observed in the mixture of these compounds. In this study, support vector regression (SVR) was applied to modeling the overlapped polarograms. Furthermore, a comparison was made between the performance of SVR and partial least square (PLS) on data set. The experimental calibration matrix was designed with 30 mixtures of these compounds. Calibration graphs were linear in the range of 6 × 10⁻⁸-10⁻⁴ and 10⁻⁷-10⁻⁴ M for isoniazid and rifampicin, respectively. The results demonstrated that SVR is a good well-performing alternative for the analysis and modeling of DPP data than the commonly applied PLS technique.     

Structure,texture and surface acidity studies of a series of mixed zinc–aluminum (60–90 molar % Al) phosphate catalysts

A series of mixed zinc–aluminum phosphate (ZnAlP) catalysts containing 40–90 aluminum molar % were synthesized by a coprecipitation method and characterized by nitrogen adsorption–desorption, X‐ray diffraction, FTIR spectroscopy, thermogravimetric analysis (TGA), differential thermal analysis (DTA), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and temperature programmed desorption (TPD) of ammonia. The presence of aluminum greatly affected the surface properties of Zn₃(PO₄)₂ by delaying the crystallization process of Zn₃(PO₄)₂. All amorphous samples were shown to be mesoporous and they contained two types of aluminum surface hydroxyl groups and one type of phosphorus hydroxyl group, as shown by DRIFT spectra. The specific surface area and the acidity of ZnAlP increased on increasing the aluminum content. On the other hand, a great difference in the texture and the concentration of surface acid sites was found by changing the precipitating agent and calcination temperature. Thus these factors also play an important role in the final properties of these catalysts. © 2001 Society of Chemical Industry