The efficient removal of lead ions at low concentrations is paramount in combating the significant threat posed by water pollution resulting from industrial activities and population growth. In this study, electrospun /PAN fibers were developed to efficiently remove lead(II) ions from water. The morphology, structure, and mechanical properties of the fibers were examined, highlighting that the augmentation of the surface area through the conversion of into the polymer fibers facilitates increased metal bonding sites during sorption. /PAN fibers exhibited superior characteristics, including higher surface area, smaller pore size, and increased pore volume, compared to powdered . The effects of factors such as shaking time, algae percentage, sorbent amount, pH, metal concentration, and temperature on Pb(II) sorption were investigated by the batch method. At an initial ion concentration of 100 μg L and pH 4.0, (5 wt %)/PAN fiber demonstrated a notable sorption efficiency of 89-90% (270 μg/g) after 60 min. The equilibrium data align with the Freundlich and Dubinin-Radushkevich isotherm models, whereas the pseudo-second-order kinetic model provides the most suitable description. The characterization of fibers after sorption revealed that carboxyl, hydroxyl, and sulfonyl groups play an active role in Pb(II) sorption.
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http://dx.doi.org/10.1021/acsabm.4c00550 | DOI Listing |
Polymers (Basel)
December 2024
Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices, Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials, State Key Laboratory of Luminescent Materials and Devices, South China Advanced Institute for Soft Matter Science and Technology, School of Emergent Soft Matter, South China University of Technology, Guangzhou 510640, China.
Phase change fibers (PCFs) can effectively store and release heat, improve energy efficiency, and provide a basis for a wide range of energy applications. Improving energy storage density and preserving flexibility are the primary issues in the efficient manufacture and application development of PCFs. Herein, we have successfully fabricated a suite of flexible PCFs with high energy storage density, which use hollow carbon fibers (HCFs) encapsulated phase change materials (PCMs) to provide efficient heat storage and release, thereby enhancing energy efficiency and underpinning a broad range of energy applications.
View Article and Find Full Text PDFBiomedicines
November 2024
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Urology, Peking University Cancer Hospital & Institute, Beijing 100089, China.
Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1 (PLOD1) is known as an enhancer of collagen fiber deposition and cross-linking stability. However, there is limited information on its function in tumors. In this study, we aimed to elucidate the function and potential mechanism of action of PLOD1 across cancers.
View Article and Find Full Text PDFACS Appl Eng Mater
December 2024
Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States.
Carbon-based nanofibers are critical materials with broad applications in industries such as energy, filtration, and biomedical devices. Polyacrylonitrile (PAN) is a primary precursor for carbon nanofibers, but conventional electrospinning techniques typically operate at low production rates of 0.1-1 mL/h from a single spinneret, limiting scalability.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Energy and Environment, City University of Hong Kong, Kowloon 999077, Hong Kong.
Digit Health
December 2024
Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China.
Background: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes, and its early identification is crucial for improving patient outcomes. Corneal confocal microscopy (CCM) can non-invasively detect changes in corneal nerve fibers (CNFs), making it a potential tool for the early diagnosis of DPN. However, the existing CNF analysis methods have certain limitations, highlighting the need to develop a reliable automated analysis tool.
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