In this present work, porous mullites (PM0–5) were synthesized through a template-assisted method using various weight percentages of pluronic (P-123). PM5 obtained using 10 wt% of P-123 was found to show maximum porosity (3.8 Å) and low dielectric constant value (2.4). PM5 was functionalized using glycidyl-terminated silane and denoted as FPM and various weight percentages of FPM were reinforced with polybenzoxazine (PBZ) matrix in order to develop FPM/PBZ nanocomposites. The thermal studies indicate that 1.5 wt% of FPM/PBZ nanocomposite showed improved thermal stability with 34% char yield at 800°C and 162°C as glass transition temperature. It also exhibits low dielectric constant (2.6) than that of the neat PBZ matrix and other FPM/PBZ nanocomposites. The microscopic analysis confirms the homogenous dispersion of FPM into the PBZ polymer that has a porous morphology. The results suggest that the as-synthesized mesoporous mullite with low dielectric constant ( k), synthesized via template-assisted method can be used as a reinforcement to decrease the dielectric constant of polymeric material, which is of industrial significance.

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Availability of reliable information on extent and impact of flood becomes critical for faster and efficient disaster management planning. Access to near realtime satellite data from optical and microwave sensors on Google Earth Engine (GEE) platform helps in rapid flood inundation mapping. Also, integration of Optical and Synthetic Aperture Radar (SAR) data from Sentinel series of satellites helps in detection of landuse/landcover features like Built-up, Agriculture Lands and Water bodies under flooding. The GEE platform provides extensive library tools for simultaneous pre-processing of SAR images and cloud free optical images from multiple satellites. In this research, Differential Thresholding and Differential Smoothening algorithms developed in GEE have been applied on Pre and Post SAR images for automatic identification of flood inundation areas. The inundated areas thus delineated were validated with inundation extents provided by the National Database for Emergency Management (NDEM). The methodology of automation developed in this study is applied to severe floods occurred in Kerala, India during August 2018. The sensitivity of polarization on mapping accuracy is estimated using Vertical- Horizontal (VH) and Vertical-Vertical (VV) modes available with Sentinel-1 data. The result indicates that VV mode of polarization provides better accuracy of 96% than VH mode. The coding is implemented in GEE environment with automation to provide extent of flood inundation for efficient management and mitigation planning during flood disasters.