Quest for high performance carbon capture membranes: Fabrication of SAPO-34 and CNTs-doped polyethersulfone-based mixed-matrix membranes

Gas separation process is an effective method for capturing and removing CO₂ from post-combustion flue gases. Due to their various essential properties such as ability to improve process efficiency, polymeric membranes are known to dominate the market. Trade-off between gas permeability and selectivity through membranes limits their separation performance. In this study, solution casting cum phase separation method was utilized to create polyethersulfone-based composite membranes doped with carbon nanotubes (CNTs) and silico aluminophosphate (SAPO-34) as nanofiller materials. Membrane properties were then examined by performing gas permeation test, chemical structural analysis and optical microscopy. While enhancing membranes CO₂ permeance, SAPO-34 and CNTs mixture improved their CO₂/N₂ selectivity. By carefully adjusting membrane casting factors such as filler loadings. Using Taguchi statistical analysis, their carbon capture efficiency was improved. The improved mixed-matrix membrane with loading of 5 wt% CNTs and 10 wt% SAPO-34 in PES showed highly promising separation performance with a CO₂ permeability of 319 Barrer and an ideal CO₂/N₂ selectivity of 12, both of which are within the 2008 Robeson upper bound. A better mixed-matrix membrane with outstanding CO₂/N₂ selectivity and CO₂ permeability was produced as a result of the synergistic effect of adding two types of fillers in optimized loading.     

Electrical Contacts With 2D Materials: Current Developments and Future Prospects

Current electrical contact models are occasionally insufficient at the nanoscale owing to the wide variations in outcomes between 2D mono and multi-layered and bulk materials that result from their distinctive electrostatics and geometries. Contrarily, devices based on 2D semiconductors present a significant challenge due to the requirement for electrical contact with resistances close to the quantum limit. The next generation of low-power devices is already hindered by the lack of high-quality and low-contact-resistance contacts on 2D materials. The physics and materials science of electrical contact resistance in 2D materials-based nanoelectronics, interface configurations, charge injection mechanisms, and numerical modeling of electrical contacts, as well as the most pressing issues that need to be resolved in the field of research and development, will all be covered in this review.     

Multicomponent Syntheses based upon Copper‐Catalyzed Alkyne‐Azide Cycloaddition

The copper‐catalyzed alkyne‐azide cycloaddition (CuAAC) is a highly versatile, regioselective synthesis of 1,4‐disubstituted 1,2,3‐triazoles under mild reaction conditions and has found numerous applications in medicinal, bioorganic, and materials chemistry in the past one and a half decades. By virtue of the enormous tolerance for functional groups and the mild reaction conditions, CuAAC has become increasingly important in combination with multicomponent reactions (MCR), either in a domino or in a consecutive fashion. While the majority of CuAAC‐based MCR are founded on the in situ or en route generation of azides, one‐pot generation of alkynes and the concatenation with other MCR are rapidly catching up and novel sequences for efficient one‐pot syntheses of triazole‐based structures in a multicomponent fashion are constantly evolving. This review summarizes important contributions of CuAAC‐based MCR including MCR‐type applications in polymer science.

     

Erucic acid ameliorates the lipopolysaccharide-induced memory deficit in rats through inhibited inflammation cytokines expression/caspase 3/NF-κB pathways

Erucic acid is a single unsaturated fatty acid that falls under the omega-9 fatty acid family. It was suggested to treat Wistar rats with lipopolysaccharide (LPS)-induced memory impairment and minimize cognitive impairment. A total of 30 animals were randomized: group I was normally treated group, group II was administered with LPS, group III was treated with LPS along with erucic acid at the dose of 10 mg kg^–1 p.o.^–1, group IV was treated with LPS along with erucic acid at 20 mg kg^–1 p.o.^–1 and group V was the erucic acid per se group provided at the dose of 20 mg kg^–1 p.o.^–1 per se. Behavioral tests were evaluated by using the Morris water maze and Y-maze. Biochemical analysis including acetylcholine esterase (AChE), choline acetyltransferase (ChAT), glutathione (GSH), catalase activity (CAT), superoxide dismutase (SOD), and nitric oxide (NO) along with proinflammatory mediators tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), caspase 3, and neuroinflammatory biomarker (nuclear factor kappa B-NF-κB) were measured. Erucic acid produced substantial behavioral improvement in the Y-maze test, including spontaneous alterations and reduced latency time during acquisition, and a longer duration of time in the consolidation phase undergoing the MWM test. Furthermore, erucic acid improved the AChE, proinflammatory markers, and oxidative stress as well as restoring endogenous antioxidant levels, ChAT, caspase 3, and NF-κB levels. Erucic acid may be a therapeutic component for conditions related to memory disorders such as memory impairment, enhances memory functioning, and protects against neuronal damage.     

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the features extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be better utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.     

Mine Wastewater Treatment with Upflow Anaerobic Fixed Film Reactors

Mine Water and the Environment - Microbial bioremediation of metals in wastewater by sulfate-reducing bacteria (SRB) has received much attention due to its high efficiency, eco-friendly techniques,...