The influence of calcium carbonate (CaCO3) on the alkali-activation kinetics, microstructure, and compressive strength of alkali-activated metakaolin (MK) geopolymer cements was investigated herein. Experimental resul...
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In this study, thermo-sensitive poly(N-isopropyl acrylamide) (PNP) was polymerized with pH-sensitive poly(acrylic acid) (PAA) to prepare a PAA-b-PNP block copolymer. Above its cloud point, the block copolymer self-ass...
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Liposomes, spherical phospholipid vesicles with a unique morphology mimicking that of body cells, have emerged as versatile nanoparticles for drug delivery. Their biocompatibility, low cytotoxicity, targeted delivery,...
Liposomes, spherical phospholipid vesicles with a unique morphology mimicking that of body cells, have emerged as versatile nanoparticles for drug delivery. Their biocompatibility, low cytotoxicity, targeted delivery, and hydrophobic and hydrophilic characteristics make them stand out over traditional drug delivery systems. Liposomes can be tailored in size, composition, lamellarity, and surface charge, offering a unique level of customization for various applications. Extensive research in liposome technology has led to the development of a wide range of liposomal formulations with enhanced functionalities, such as PEGylated liposomes, ligand-targeted liposomes, and stimuli-responsive liposomes. Beyond their crucial role in cancer treatment, liposomes play a significant role in influenza, COVID-19, cancer, and hepatitis A vaccines. They are also utilized in pain management, fungal treatment, brain targeting, and topical and ocular drug delivery. This review offers insight into the types of liposomes, their composition, preparation methods, characterization methods, and clinical applications. Additionally, it discusses challenges and highlights potential future directions in liposome-based drug delivery.
The stepped tetragonal phase single-crystalline BaTiO 3 nano-microstructure composed of cubes evolved from nanoparticles were synthesized via a simple solvothermal method without template or surfactant...
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The influence of calcium carbonate (CaCO3) on the alkali-activation kinetics, microstructure, and compressive strength of alkali-activated metakaolin (MK) geopolymer cements was investigated herein. Experimental resul...
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Domain walls, topological defects that define the frontier between regions of different stacking order in multilayer graphene, have proved to host exciting physics. The ability to tune these topological defects in sit...
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Domain walls, topological defects that define the frontier between regions of different stacking order in multilayer graphene, have proved to host exciting physics. The ability to tune these topological defects in situ in an electronic transport experiment brings a wealth of possibilities in terms of fundamental understanding of domain walls as well as for electronic applications. Here, we demonstrate, through a MEMS (microelectromechanical system) actuator and magnetoresistance measurements, the effect of domain walls in multilayer graphene quantum Hall effect. Reversible and controlled uniaxial strain triggers the topological defects, manifested as addtional quantum Hall effect plateaus as well as a discrete and reversible modulation of the current across the device. Our findings are supported by theoretical calculations and constitute indication of the in situ tuning of topological defects in multilayer graphene probed through electronic transport, opening the way for the use of reversible topological defects in electronic applications.
Aqueous zinc-based energy storage devices possess superior safety, cost-effectiveness, and high energy density;however, dendritic growth and side reactions on the zinc electrode curtail their widespread applications. ...
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Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse c...
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Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space.
Polymeric compositesComposite reinforced by lignocellulosic fibersLignocellulosic fibers are an eco-friendly option for various applications that utilize the benefits of natural fibersNatural fibers and polymersPolyme...
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Objectives: The heterogeneity and phenotype of immune cells orchestrate many physiologic and pathologic processes. Recent evidence suggests that immune cells play critical roles in the progression of osteoarthritis (O...
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