Hierarchical porous poly(l-lactic acid)/SiO2 nanoparticles fibrous membranes for oil/water separation
Source abstract
Abstract
Abstract A two-step strategy has been developed to introduce silica nanoparticles into highly porous poly(l-lactic acid) (PLLA) nanofibers. Silica nanoparticles (SiNPs) were firstly synthesized and then modified to be hydrophobic. After PLLA/SiNPs composite fibrous membranes were electrospun and collected, they were re-crystallized by acetone at room temperature for a few minutes. With the re-arrangement of PLLA chains, the nano-/micro-electrospun fibres were transformed from non-porous ones to be porous ones with high surface area. Consequently, SiNPs that were completely covered by PLLA before acetone treatment showed up at the fibre surface. Higher PLLA crystallization also enhanced the Young’s modulus and tensile strength (420 and 8.47 MPa) of the composite membrane. However, incorporation of SiNPs into porous PLLA membranes reduced their modulus and tensile strength (280.66 and 5.92 MPa), but an increase in strain to fracture (80.82%) was observed. Scanning electron microscopy (SEM), focused ion beam SEM, transmission electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction were applied to confirm the presence of SiNP in PLLA fibres. The presence of SiNPs inside and outside fibres enhances the hydrophobicity of PLLA/SiNPs nano-fibrous membrane as the water contact angle is greater than 150°. The oil absorption of these porous composite membranes was also tested using four different oils, which can reach the highest absorption capacity when the weight ratio of PLLA and SiNPs is 1:1. The flux of prepared membranes was investigated, and results indicated that SiNPs-loaded membrane effectively enhanced the flux (5200 Lm−2 h−1).
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Research context
OpenAlex topics: Electrospun Nanofibers in Biomedical Applications · Surface Modification and Superhydrophobicity · Supercapacitor Materials and Fabrication
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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