Mesoporous materials, Tb-mesoMOF and MCM-41, were used to study the transport phenomena of biomolecules entering the interior pores from solution. Vitamins B and B were successfully encapsulated into these mesoporous materials, whereas Tb-mesoMOF (0.33 g of B/g, 0.01 g of B/g) adsorbed a higher amount of vitamin per mass than MCM-41 (0.21 g of B/g, 0.002 g of B/g). The diffusion mechanism of the biomolecules entering Tb-mesoMOF was evaluated using a mathematical model. The Raman spectroscopy studies showed vitamin B has been encapsulated within Tb-mesoMOF's pores, and evaluation of the peak shifts indicated strong interactions linking vitamin B's pyrroline moiety with Tb-mesoMOF's triazine and benzoate rings. Because of these stronger interactions between the vitamins and Tb-mesoMOF, longer egress times were observed than with MCM-41.
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http://dx.doi.org/10.1021/acsami.7b00588 | DOI Listing |
Sci Rep
December 2024
School of architecture, Ocean and energy power engineering, Wuhan University of Technology, Wuhan, 430070, China.
During maritime operations, extreme events such as explosions, grounding, and seal failures can cause water ingress into lubricant compartments, forming oil-water emulsions that significantly affect the lubrication performance of ship stern bearings. Existing studies mainly focus on low water content, with limited exploration of the impact of high water content on lubrication performance. To address this gap, viscosity measurements of oil-water mixtures were conducted, and an emulsification viscosity equation applicable to varying water contents was derived.
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December 2024
Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia.
The world is moving towards the utilization of hydrogen vehicle technology because its advantages are uniformity in power production, more efficiency, and high durability when compared to fossil fuels. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) device is selected for producing the energy for the hydrogen vehicle. The merits of this fuel technology are the possibility of operating less source temperature, and more suitability for stationery and transportation applications.
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December 2024
Faculty of Natural Sciences, Institute of Biology, Biotechnology and Environmental Protection, University of Silesia in Katowice, Jagiellońska 28, 40-032, Katowice, Poland.
Studies of in situ plant response and adaptation to complex environmental stresses, are crucial for understanding the mechanisms of formation and functioning of ecosystems of anthropogenically transformed habitats. We study short- and long-term responses of photosynthetic apparatus (PSA) and anti-oxidant capacity to complex abiotic stresses of common plants Calamagrostis epigejos and Solidago gigantea in semi-natural (C) and heavy metal contaminated habitats (LZ). We found significant differences in leaf pigment content between both plant species growing on LZ plots and their respective C populations.
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December 2024
Division of Plant Science and Technology, University of Missouri, Columbia, MO, 65211, USA.
The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, has evolved resistance to nearly every management tactic utilized in the field. This study investigated the resistance mechanisms in a WCR strain resistant to the Bacillus thuringiensis (Bt) protein eCry3.1Ab using dsRNA to knockdown WCR midgut genes previously documented to be associated with the resistance.
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December 2024
School of Public Health, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
The study aims to address the critical issue of toxic side effects resulting from drug combinations, which can significantly increase health risks, clinical complications, and lead to drug being withdrawn from the market. A model named TSEDDI (toxic side effects of drug-drug interaction) has been developed to improve the identification of drug pairs that may induce toxicity or adverse reactions. By utilizing drug chemical structures and diverse proteins, we employ a convolutional neural network (CNN) to extract features from molecular images, enzyme proteins, transporter proteins, and target proteins.
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