Search

The present work attempts to estimate the impact of graphene and carbon fabric on the dynamic‐mechanical properties of epoxy nanocomposites with different weight percentages. By applying an oscillatory force to a composite, its response is measured, and by calculating the viscosity and the stiffness in relation to the temperature, time, or frequency, valuable information can be realized. It helps to identify "thermal transitions" occurring in polymers. The complex modulus shows the resistance of the composites to deformation caused by viscous and elastic effects. Though dynamic mechanical analysis (DMA) is used for the depiction of temperature‐dependent viscoelastic properties, in this investigation, the objectives were multi‐fold. It is aimed at understanding the role of carbon fabric and the incorporation of 1 wt% of graphene nanoplatelet (GNP) in the epoxy matrix, and the effect of 0.5 to 5 wt% of GNP. The dynamic mechanical properties, including storage modulus, loss modulus, and damping factor, are examined across a temperature range of 25–250°C. Based on the findings, the storage modulus of the composites exhibits a range between 2000 and 9000 MPa. Notably, the epoxy composite containing 1 wt% GNP filler demonstrates the highest storage modulus. The details of mechanisms involved in DMA measurement are schematically summarized. Highlights: Epoxy‐GNP and Epoxy‐GNP‐carbon fabric composites were fabricated.Storage modulus of composites does not show much dependence on the GNP.An increase in storage modulus by 05 times is observed in the ECG1 composite.The loss modulus of EC and EG1 composite is much higher. [ABSTRACT FROM AUTHOR]

Image restoration (IR) attempts to recreate the original (ideal) scene from a degraded observation. The goal of image restoration is to avert the process of image deterioration that happens during image acquisition and processing. Blurring and noise are two common types of picture capture degradation. When the blurring function is unknown, the image restoration issue is referred to as blind image restoration. Existing methods of image restoration do not provide efficient results due to time complexity, computational complexity, large-scale scaling factor and limited input. Therefore, in order to overcome those problems, an Improved Graph Laplacian Regularization with Sparse Coding by integrating Internet of Things (IoT) is developed in this research. The process of removing de-noised portions of image to texture layer and cartoon layer is carried out by Morphological Component Analysis (MCA). An improved Graph Laplacian regularized method and Simultaneous Sparse Coding with Gaussian Scale Mixture (SSC-GSM) methods is performed on texture layer, cartoon layer and restored image. A Levin's dataset and a real-time dataset such as industrial manufacturing products are analyzed in this research. The proposed Graph Laplacian algorithm and sparse coding model are better efficient in identifying sharper texture information and getting a restored image. The proposed method proves that it has better performance in terms of Peak Signal-to-Noise Ratio (PSNR) of 35.82 and Structural Similarity Index Measure (SSIM) of 0.94 than the existing methods. [ABSTRACT FROM AUTHOR]