Reinforcement of single-walled carbon nanotubes on polydimethylsiloxane membranes for CO2, O2, and N2 permeability/selectivity.

In this study, PDMS incorporated with SWCNTs have been fabricated via solution casting method for industrial applications and characterized by the analyses of SEM, FTIR, TGA, AFM, and MST. The modified membranes were further analyzed for CO2, O2, and N2 gas permeability. The strategic membranes have five different weight ratios (0.013, 0.025, 0.038, 0.050, 0.063) compared to neat PDMS membranes. The even distribution of SWCNTs in PDMS provided results that showed improvement in thermal stability. However, mechanical strength has been weakened with increased concentration of nanofiller because of the increase in the number of SWCNTs by increases that imperfections become more severe. The designed polymeric membranes with good thermal stability and adequate mechanical strength can be used for the selectivity and permeability of CO2, O2, and N2 gases. The effect of the PDMS-SWCNTs on gas permeability has been studied. 0.063 wt.% SWCNTs presented the maximum permeability of CO2 gas while maximum O2 and N2 gas permeability have been obtained by 0.013 wt.% SWCNTs. The ideal selectivity of mixed (50:50) gas conditions has been tested. The maximum CO2/N2 ideal selectivity was obtained by 0.050 and 0.063 wt.% SWCNTs while maximum O2/N2 ideal selectivity obtained by 0.050 wt.% SWCNTs. Therefore, the fabrication of this novel SWCNTs-PDMS membrane may lead to separating the industrial exhaust and be used as a potential membrane for environmental remediation in the future.

Novel antibacterial polyurethane and cellulose acetate mixed matrix membrane modified with functionalized TiO2 nanoparticles for water treatment applications.

Bacterial contamination is one of the leading causes of water pollution. Antibacterial polyurethane/cellulose acetate membranes modified by functionalized TiO2 nanoparticles were processed and studied. TiO2 nanoparticles were prepared and ultraviolet (UV) irradiated to activate their photocatalytic activity against Escherichia coli (E. Coil) and Methicillin-resistant Staphylococcus aureus (MRSA) bacteria. Functionalized TiO2 nanoparticles were incorporated in flat-sheet mixed matrix membranes (MMMs). These membranes were characterized for their different properties such as morphology, thermal stability, mechanical strength, surface wettability, water retention, salt rejection, water flux, and their antibacterial performance against E. Coil and MRSA was also tested. The activity of nanoparticles against MRSA and E. coli was analyzed using three different concentrations, 0.5 wt%, 1.0 wt% and 1.5 wt% of nanoparticles and 0.5 wt% of TiO2 nanoparticles showed maximum growth of bacteria. The maximum inhibition was observed in membranes with maximum nanoparticles when compared with other membranes. All these characteristics were strongly affected by increasing the concentration of TiO2 nanoparticles in the prepared membranes and the duration of their UV exposure. Hence, it was proved from this analysis that these TiO2 modified membranes exhibit substantial antibacterial properties. The results are supporting the utilization of these materials for water purification purposes.

Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach.

The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.

Synthesis, in vitro antiurease, in vivo antinematodal activity of quinoline analogs and their in-silico study.

Synthesis of quinoline analogs and their urease inhibitory activities with reference to the standard drug, thiourea (IC50 = 21.86 ± 0.40 µM) are presented in this study. The inhibitory activity range is (IC50 = 0.60 ± 0.01 to 24.10 ± 0.70 µM) which displayed that it is most potent class of urease inhibitor. Analog 1-9, and 11-13 emerged with many times greater antiurease potential than thiourea, in which analog 1, 2, 3, 4, 8, 9, and 11 (IC50 = 3.50 ± 0.10, 7.20 ± 0.20, 1.30 ± 0.10, 2.30 ± 0.10, 0.60 ± 0.01, 1.05 ± 0.10 and 2.60 ± 0.10 µM respectively) were appeared the most potent ones among the series. In this context, most potent analogs such as 1, 3, 4, 8, and 9 were further subjected for their in vitro antinematodal study against C. elegans to examine its cytotoxicity under positive control of standard drug, Levamisole. Consequently, the cytotoxicity profile displayed that analogs 3, 8, and 9 were found with minimum cytotoxic outline at higher concentration (500 µg/mL). All analogs were characterized through 1H NMR, 13C NMR and HR-EIMS. The protein-ligand binding interaction for most potent analogs was confirmed via molecular docking study.

Algae as an attractive source for cosmetics to counter environmental stress.

In recent times, a considerable amount of evidence has come to light regarding the effect that air pollution has on skin conditions. The human skin is the chief protection we have against environmental harm, whether biological, chemical, or physical. The stress from these environmental factors, along with internal influences, can be a cause of skin aging and enlarged pores, thinner skin, skin laxity, wrinkles, fine lines, dryness, and a more fragile dermal layer. This knowledge has led to greater demand for skin cosmetics and a requirement for natural raw ingredients with a high degree of safety and efficiency in combating skin complications. Recent developments in green technology have made the employment of naturally occurring bioactive compounds more popular, and novel extraction methods have ensured that the use of these compounds has greater compatibility with sustainable development principles. Thus, there is a demand for investigations into efficient non-harmful naturally occurring raw ingredients; compounds derived from algae could be beneficial in this area. Algae, both macroalgae and microalgae, consists of waterborne photosynthetic organisms that are potentially valuable as they have a range of bioactive compounds in their composition. Several beneficial metabolites can be obtained from algae, such as antioxidants, carotenoids, mycosporine-like amino acids (MAA), pigments, polysaccharides, and scytonemin. Various algae strains are now widely employed in skincare products for various purposes, such as a moisturizer, anti-wrinkle agent, texture-enhancing agents, or sunscreen. This research considers the environmental stresses on human skin and how they may be mitigated using cosmetics created using algae; special attention will be paid to external factors, both generally and specifically (amongst them light exposure and pollutants).

Improving Lithium-Ion Half-/Full-Cell Performance of WO3 -Protected SnO2 Core-Shell Nanoarchitectures.

Anodes derived from SnO2 offer a greater specific capacity comparative to graphitic carbon in lithium-ion batteries (LIBs); hence, it is imperative to find a simple but effective approach for the fabrication of SnO2 . The intelligent surfacing of transition metal oxides is one of the favorite strategies to dramatically boost cycling efficiency, and currently most work is primarily aimed at coating and/or compositing with carbon-based materials. Such coating materials, however, face major challenges, including tedious processing and low capacity. This study successfully reports a new and simple WO3 coating to produce a core-shell structure on the surface of SnO2 . The empty space permitted natural expansion for the SnO2 nanostructures, retaining a higher specific capacity for over 100 cycles that did not appear in the pristine SnO2 without WO3 shell. Using WO3 -protected SnO2 nanoparticles as anode, a coin half-cell battery was designed with Li-foil as counter-electrode. Furthermore, the anode was paired with commercial LiFePO4 as cathode for a coin-type full cell and tested for lithium storage performance. The WO3 shell proved to be an effective and strong enhancer for both current rate and specific capacity of SnO2 nanoarchitectures; additionally, an enhancement of cyclic stability was achieved. The findings demonstrate that the WO3 can be used for the improvement of cyclic characteristics of other metal oxide materials as a new coating material.