Recently, considerable interest has been focused on developing greener and biodegradable materials due to growing environmental concerns. Owing to their low cost, biodegradability, and good mechanical properties, plant fibers have substituted synthetic fibers in the preparation of composites. However, the poor interfacial adhesion due to the hydrophilic nature and high-water absorption limits the use of plant fibers as a reinforcing agent in polymer matrices. The hydrophilic nature of the plant fibers can be overcome by chemical treatments. Cellulose the most abundant natural polymer obtained from sources such as plants, wood, and bacteria has gained wider attention these days. Different methods, such as mechanical, chemical, and chemical treatments in combination with mechanical treatments, have been adopted by researchers for the extraction of cellulose from plants, bacteria, algae, etc. Cellulose nanocrystals (CNC), cellulose nanofibrils (CNF), and microcrystalline cellulose (MCC) have been extracted and used for different applications such as food packaging, water purification, drug delivery, and in composites. In this review, updated information on the methods of isolation of nanocellulose, classification, characterization, and application of nanocellulose has been highlighted. The characteristics and the current status of cellulose-based fiber-reinforced polymer composites in the industry have also been discussed in detail.

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This article emphasizes the optimization of processing parameters required for the blending of polyether-based thermoplastic polyurethane (TPU) and poly dimethyl siloxane (PDMS) rubber. Taguchi methodology was adapted as a statistical tool in the design of experiment to optimize processing conditions on the basis of tensile strength and impact strength for 50:50 blend of TPU and PDMS. L 9 (3 3 ) orthogonal array strategy was utilized for conducting the experiments. The experimental data were transformed into signal-to-noise ratios, based on which each processing parameter has been optimized. Measure of confidence of the results has been studied using standard statistical technique of analysis of variance. From the results, the processing conditions have been optimized.

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