For applications in energy storage and conversion, many metal oxide (MO)/C composite fibers have been synthesized using cellulose as the template. However, MO particles in carbon fibers usually experience anomalous growth to a size of >200 nm, which is detrimental to the overall performance of the composite. In this paper, we report the successful development of a generic approach to synthesize a fiber composite with highly dispersed MO nanoparticles (10-80 nm) via simple swelling, nitrogen doping, and carbonization of the cellulose microfibril. The growth of the MO nanoparticles is confined by the structure of the microfibrils. Density functional theory calculation further reveals that the doped N atoms supply ample nucleation sites for size confinement of the nanoparticles. The encapsulation structure of small MO nanoparticles in the conductive carbon matrix improves their electrochemical performance. For example, the formed SnO/carbon nanocomposite exhibits high specific capacities of 1011.0 mA h g at 0.5 A g and 581.8 mA h g at 5 A g. Moreover, the fiber-like nanocomposite can be combined with carbon nanotubes to form a flexible binder-free electrode with a capacity of ∼10 mA h cm, far beyond the commercial level. The process developed in this study offers an alternative approach to sophisticated electrospinning for the synthesis of other fiber-like MO/carbon nanocomposites for versatile applications.
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http://dx.doi.org/10.1021/acsami.0c03390 | DOI Listing |
Appl Microbiol Biotechnol
January 2025
Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe, 657-8501, Japan.
The fermentative production of valuable chemicals from lignocellulosic feedstocks has attracted considerable attention. Although Saccharomyces cerevisiae is a promising microbial host, it lacks the ability to efficiently metabolize xylose, a major component of lignocellulosic feedstocks. The xylose oxidative pathway offers advantages such as simplified metabolic regulation and fewer enzymatic steps.
View Article and Find Full Text PDFBiomed Microdevices
January 2025
Department of Physics, Faculty of Philosophy, Science and Letter, University of São Paulo, Ribeirão Preto, SP, 14040-901, Brazil.
The overexpression of Human Epidermal Growth Factor Receptor 2 (HER2) protein is specifically related to tumor cell proliferation in breast cancers. Its presence in biological serum samples indicates presence or progression of cancer, becoming a promise biomarker. However, their detection needs a simple and high accuracy platform.
View Article and Find Full Text PDFNanoscale
January 2025
Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, USA.
Controlled synthesis of faceted nanoparticles on surfaces without explicit use of ligands has gained attention due to their promising applications in electrocatalysis and chemical sensing. Electrodeposition is a desirable method; however, precise control over their size, spatial distribution, and morphology requires extensive optimization. Here, we report the spatially resolved synthesis of shape-controlled Pt nanoparticles and fast screening of synthesis conditions in scanning electrochemical cell microscopy (SECCM) with pulse potentials.
View Article and Find Full Text PDFBioengineered
December 2025
Department of Translational Medical Bioengineering, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine.
This article presents new data on the integrated use of colloidal solutions of nanoparticles and low-intensity laser radiation on the biosynthetic activity of the medicinal mushroom . Traditional mycological methods, colloidal solutions of biogenic metals, and unique photobiological methods have also been used. It was found that colloidal solutions of nanoparticles of all metals used increased the growth characteristics of (55-60%), while irradiation of the fungal inoculum with laser light in a medium with nanoparticles reduced the growth activity of mycelia by 12.
View Article and Find Full Text PDFIET Syst Biol
January 2025
School of Computer Science and Technology, Baotou Medical College, Baotou, China.
Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, and signal transduction. Predicting the protein-metal ion ligand binding residues (PMILBRs) accurately is a challenging task in theoretical calculations. In this study, the authors employed fused amino acids and their derived information as feature parameters to predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results.
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