Improving Performance of the Synthesis of Silica Nanoparticles by Surfactant-incorporated Wet Attrition Milling
Source abstract
Abstract
Abundant applications of silica nanoparticles (SiNPs) have motivated many research groups for silica nanoparticles synthesis with tuned properties. Here we show the formation of SiNPs using the top-down method in a wet attrition mill with effective use of surfactants sodium laurylsulfate (SLS) and Tween-80. The attrition milling was facilitated by using zirconium beads as milling media. The effects of surfactant concentration on particle size distribution have been studied with a particle size analyzer using the dynamic light scattering technique (DLS). The optimum conditions for wet milling with surfactant concentration were evaluated experimentally. The field emission scanning electron microscopy (FE-SEM) analysis confirmed particle size and particle shape. The energy-dispersive X-ray spectroscopy (EDX) exhibited the presence of silicon and oxygen as elemental constituents of SiNPs. The FEG - Transmission electron microscope and HR-TEM analysis identified SiNPs of spherical shape in the size range from 21 nm to 46 nm. The X-ray diffraction (XRD) peaks revealed the presence of SiNPs polycrystallite size as 35.54 nm and 40.63 nm for 5 wt % Tween-80 and 5 wt% SLS, respectively. The selected area electron diffraction (SAED) pattern analysis exhibited a polycrystalline structure of SiNPs. Fourier transform infrared spectroscopy (FTIR) peaks confirmed the purity of SiNPs and the presence of Si-O bond. The foam formation and foam stability were analyzed to explore the effects of foam formation on the wet milling process. These experimentations propose the effective use of surfactants in the milling method for the production of SiNPs. Graphical Abstract
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Minerals Flotation and Separation Techniques · Mineral Processing and Grinding · Clay minerals and soil interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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