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In this study, hydrophobic silica aerogels were synthesized from rice husk ash-derived sodium silicate through sol-gel processing, solvent exchange, surface modification and ambient pressure drying. By volume, 10% of trimethylchlorosilane (TMCS) in 90% of n-hexane was used as a hydrophobic solution in the surface modification process. The physical and chemical properties of silica aerogels were characterized by density and porosity measurements, scanning electron microscopy (SEM), Fourier transforms infrared (FTIR) spectroscopy, Brunauer–Emmett–Teller theory (BET) and dynamic scanning calorimetry (DSC). The hydrogels prepared were in the form of 2.5 ± 0.5 mm beads and then converted into alcogels through solvent exchange with ethanol for repetition of 3, 6 and 9 days. It is found that the optimal quality of silica aerogels with the BET surface area as high as 668.82 m2/g was obtained from the alcogels of the solvent exchange period of 9 days. Depending on the size of the gel’s block, a longer solvent exchange period will ensure adequate removal of pore water. Post heat treatment on silica aerogels obtained from the 9 days of solvent exchange at 200, 300 and 400 °C for 2 h results in slight decreased of aerogel’s density from 0.048 g/cm3 to 0.039 g/cm3 and the hydrophobicity of the aerogels is decreased above 380 °C as confirmed by DSC analysis.
相似文献The satellite-based regression model provides the data model that identifies water quality for inland and coastal waters. However, the satellite regression usually depends on the selection of observation, satellite data, and model type. A resampling simulation technique, such as sequential simulation using geographically weighted regression (GWR simulation), can be applied in generating multiple realizations for water quality estimation to reduce the sampling effect and consider spatial heterogeneity. Traditional models often result in considerable underestimation in extreme observations. The GWR simulation provides the best goodness of fit and spatial varying relationship between observed water quality and remote sensing considering parameter outlier and noise removal for parameter stability. This simulation model can increase the sampling diversity from various observations and reduce the neighboring effects of observations using outlier and noise removal. The model that handles spatial uncertainty and heterogeneity is a novel tool for inferring the characteristics of water quality from a series of sample subsets.
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