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Membrane technology is a simple and energy-conservative separation option that is considered to be a green alternative for CO2 capture processes. However, commercially available membranes still face challenges regarding water and chemical resistance. In this study, the effect of water and organic contaminants in the feed stream on the CO2/CH4 separation performance is evaluated as a function of the hydrophilic and permselective features of the top layer of the membrane. The membranes were a commercial hydrophobic membrane with a polydimethylsiloxane (PDMS) top layer (Sulzer Chemtech) and a hydrophilic flat composite membrane with a hydrophilic [emim][ac] ionic liquid-chitosan (IL-CS) thin layer on a commercial polyethersulfone (PES) support developed in our laboratory. Both membranes were immersed in NaOH 1M solutions and washed thoroughly before characterization. The CO2 permeance was similar for both NaOH-treated membranes in the whole range of feed concentration (up to 250 GPU). The presence of water vapor and organic impurities of the feed gas largely affects the gas permeance through the hydrophobic PDMS membrane, while the behavior of the hydrophilic IL-CS/PES membranes is scarcely affected. The effects of the interaction of the contaminants in the membrane selective layer are being further evaluated.Two noble metals, such as silver and gold alloy nanoparticles, were successfully synthesized by the microwave assisted method in the presence of the Asparagus racemosus root extract and were used as an antibacterial and immunomodulatory agent. The nanostuctures of the synthesized nanoparticles were confirmed by various spectroscopic and microscopic techniques. The UV-vis spectrum exhibits a distinct absorption peak at 483 nm for the bimetallic alloy nanoparticles. The microscopic analysis revealed the spherical shaped morphology of the biosynthesized nanoparticles with a particle size of 10-50 nm. The antibacterial potential of the green synthesized single metal (AgNPs and AuNPs) and bimetallic alloy nanoparticles was tested against five bacterial strains. The bimetallic alloy nanoparticles displayed the highest zone of inhibition against P. aeurgnosia and S.aureus strains when compared to single metal nanoparticles and plant extract. In addition, the inmmunomodulatory potential of the root extract of A. racemosus, AgNPs, AuNPs, and Ag-Au alloy NPs is achieved by measuring the cytokine levels in macrophages (IL-1β, IL-6, and TNF-α) and NK cells (IFN-γ) of NK92 and THP1 cells using the solid phase sandwich ELISA technique. The results showed that the root extract of A. racemosus, AgNPs, and AuNPs can reduce the pro-inflammatory cytokine levels in the macrophages cells, while Ag-Au alloy NPs can reduce cytokine responses in NK92 cells. Overall, this study shows that the microwave assisted biogenic synthesized bimetallic nanoalloy nanoparticles could be further explored for the development of antibacterial and anti-inflammatory therapies.Titanium implants are commonly used in the field of dentistry for prosthetics such as crowns, bridges, and dentures. For successful therapy, an implant must bind to the surrounding bone in a process known as osseointegration. The objective for this ongoing study is to determine the potential of different implant surface coatings in providing the formation of hydroxyapatite (HA). The coatings include titanium nitride (TiN), silicon dioxide (SiO2), and quaternized titanium nitride (QTiN). The controls were a sodium hydroxide treated group, which functioned as a positive control, and an uncoated titanium group. Each coated disc was submerged in simulated body fluid (SBF), replenished every 48 h, over a period of 28 days. Each coating successfully developed a layer of HA, which was calculated through mass comparisons and observed using scanning electron microscopy (SEM) and energy dispersive analysis x-rays (EDX). Among these coatings, the quaternized titanium nitride coating seemed to have a better yield of HA. Further studies to expand the data concerning this experiment are underway.The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. https://www.selleckchem.com/products/ph-797804.html For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.
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