ABSTRACT
Nanocellulose has emerged in recent years as one of the most notable green materials available due to its numerous appealing factors, including its non-toxic nature, biodegradability, high aspect ratio, superior mechanical capabilities, remarkable optical properties, anisotropic shape, high mechanical strength, excellent biocompatibility and tailorable surface chemistry. It is proving to be a promising material in a range of applications pertinent to the material engineering to biomedical applications. In this review, recent advances in the preparation, modification, and emerging application of nanocellulose, especially cellulose nanocrystals (CNCs), are described and discussed based on the analysis of the latest investigations. This review presents an overview of general concepts in nanocellulose-based nanocomposites for sustainable applications. Beginning with a brief introduction of cellulose, nanocellulose sources, structural characteristics and the extraction process for those new to the area, we go on to more in-depth content. Following that, the research on techniques used to modify the surface properties of nanocellulose by functionalizing surface hydroxyl groups to impart desirable hydrophilic-hydrophobic balance, as well as their characteristics and functionalization strategies, were explained. The usage of nanocellulose in nanocomposites in versatile fields, as well as novel and foreseen markets of nanocellulose products, are also discussed. Finally, the difficulties, challenges and prospects of materials based on nanocellulose are then discussed in the last section for readers searching for future high-end eco-friendly functional materials.
ABSTRACT
The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.
