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The effects of the addition of colloidal silica (cSiO2) and solvents used in the catalyst preparation on the activity and stability of WO3-TiO2 catalysts are reported in this paper. The highly stable and selective WO3 supported cSiO2-TiO2 catalysts were prepared and tested in the vapor-phase glycerol oxy-dehydration. WO3-TiO2 catalysts with and without cSiO2 were characterized by XRD, SEM, NH3-TPD, infrared spectroscopy of pyridine (FTIR-Py), XPS, RAMAN, and N2 adsorption-desorption (BET). The highest medium strength acidity and optimum Brønsted to Lewis acid site ratio of WO3 catalysts were achieved upon the addition of colloidal silica (cSiO2) onto TiO2 support. The medium strength acidity of Brønsted acid sites was responsible for the improved acrolein selectivity and stability. The other major factors in glycerol conversion and acrolein selectivity were the glycerol content and liquid hourly space velocity. The yield to acrolein was up to 70% and kept almost constant in a 50 h continuous run at 300 °C. The gradual decrease in glycerol conversion was due to the build-up of oxygen-containing carbonaceous materials deposited on the catalyst surface. [ABSTRACT FROM AUTHOR]

The catalytic performance of tungsten oxide, WO3-KIT-6 in the selective dehydration of glycerol to acrolein was studied. The chemical and structural properties of the catalysts were examined by X-ray diffraction (XRD), nitrogen adsorption–desorption (BET) isotherms, temperature-programmed ammonia (TPD-NH3) desorption, and X-ray photoelectron spectroscopy (XPS), SEM, TGA, Raman and pyridine adsorption FTIR. Catalysts synthesized by the hydrolytic sol–gel (HSG) method showed quantitative conversion of glycerol and acrolein selectivity. The medium-strength surface acidity of the catalyst and mesoporosity played a crucial role in the dehydration of glycerol to acrolein. The maximum stable acrolein selectivity of 55% at 98% glycerol conversion was obtained over 25 wt%W-KIT-6(100) (7.9 nm average pore size) catalyst having the highest concentration of medium-strength acid sites. The effects of mesopore sizes and the selective dehydration of glycerol were elucidated. The catalyst with the largest mesopores diameter displayed higher reactivity, longer lifetime, and improved selectivity. [ABSTRACT FROM AUTHOR]

Background: Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. Purpose: To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. Material and Methods: A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. Results: Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. Conclusion: Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability. [ABSTRACT FROM AUTHOR]

In our study, the synthesis of zeolitic imidazolate framework (ZIF-12) crystals and the preparation of mixed matrix membranes (MMMs) with various ZIF-12 loadings were targeted. The characterization of ZIF-12 and MMMs were carried out by Fourier transform infrared spectroscopy analysis, thermogravimetric analysis, scanning electron microscopy (SEM), and thermomechanical analysis. The performance of MMMs was measured by the ability of binary gas separation. Commercial polyetherimide (PEI-Ultem® 1000) polymer was used as the polymer matrix. The solution casting method was utilized to obtain dense MMMs. In the SEM images of ZIF-12 particles, the particles with a rhombic dodecahedron structure were identified. From SEM images, it was observed that the distribution of ZIF-12 particles in the MMMs was homogeneous and no agglomeration was present. Gas permeability experiments of MMMs were measured for H2, CO2, and CH4 gases at steady state, at 4 bar and 35 °C by constant volume-variable pressure method. PEI/ZIF-12-30 wt% MMM exhibited high permeability and ideal selectivity values for H2/CH4 and CO2/CH4 were P H 2 / CH 4 = 331.41 ${P}_{{\text{H}}_{2}/{\text{CH}}_{4}}=331.41$ and P CO 2 / CH 4 = 53.75 ${P}_{{\text{CO}}_{2}/{\text{CH}}_{4}}=53.75$ gas pair. [ABSTRACT FROM AUTHOR]