Sustainable Smart Polymer Composite Materials: A Comprehensive Review

This review provides a thorough analysis of the progress made in smart polymer composite materials, which have recently been seen as potential game-changers in areas such as construction, aerospace, biomedical engineering, and energy. This article emphasizes the distinctive characteristics of these materials, including their responsiveness to stimuli like temperature, light, and pressure, and their potential uses in different industries. This paper also examines the difficulties and restrictions associated with the creation and utilization of smart polymer composite materials. This review seeks to provide a thorough understanding of smart polymer composite materials and their potential to offer innovative solutions for a variety of applications

Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review

Polymer composites are employed in a variety of applications due to their distinctive characteristics. Nevertheless, designing and optimizing these materials can be a lengthy and resourceintensive process for low cost and sustainable materials. Machine learning has the potential to simplify this process by offering predictions of the characteristics of novel composite materials based on their microstructures. This review outlines machine learning techniques and highlights the potential of machine learning to improve the design and optimization of polymer composites. This review also examines the difficulties and restrictions of utilizing machine learning in this context and offers insights into potential future research paths in this field

Extraction and Characterization of Sponge Gourd Outer Skin Fiber

Plant-derived natural fibers have recently been used as a promising reinforcement in polymer matrix composites. Fibers from the inner sponge of the luffa cylindrica fruit have been the subject of several studies as reinforcement in various polymer resins. However, no research has been done on the Sponge Gourd Outer Skin Fiber (SGOSF). The present work is aimed to extract and characterize the chemical and physical properties of novel natural lignocellulosic fiber obtained from the outer skin (waste material) of the matured Luffa Cylindrica plant fruit. Characteristic studies on chemical composition, chemical structure, chemical compounds, thermal stability, and morphology of luffa cylindrica outer skin fibers (SGOSF) were investigated. The results revealed that SGOSF had cellulose (81.69 wt. %), lignin (10.14 wt. %), wax (0.22 wt.%), and ash content (1.18 wt. %). The density of the SGOSF was reported as 1.394 g/cm3. Thermal study results indicate SGOSF were withstanding the temperature up to 320°C. Fourier transform infrared analysis (FTIR) was done to determine the molecular structure of SGOSFs. The SGOSFs show tensile strength of 438.5 MPa. These results revealed that SGOSFs could be a sound reinforcement in the polymer matrix composites for various applications

Sustainable Smart Polymer Composite Materials: A Comprehensive Review

This review provides a thorough analysis of the progress made in smart polymer composite materials, which have recently been seen as potential game-changers in areas such as construction, aerospace, biomedical engineering, and energy. This article emphasizes the distinctive characteristics of these materials, including their responsiveness to stimuli like temperature, light, and pressure, and their potential uses in different industries. This paper also examines the difficulties and restrictions associated with the creation and utilization of smart polymer composite materials. This review seeks to provide a thorough understanding of smart polymer composite materials and their potential to offer innovative solutions for a variety of applications

Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review

Polymer composites are employed in a variety of applications due to their distinctive characteristics. Nevertheless, designing and optimizing these materials can be a lengthy and resourceintensive process for low cost and sustainable materials. Machine learning has the potential to simplify this process by offering predictions of the characteristics of novel composite materials based on their microstructures. This review outlines machine learning techniques and highlights the potential of machine learning to improve the design and optimization of polymer composites. This review also examines the difficulties and restrictions of utilizing machine learning in this context and offers insights into potential future research paths in this field