Optimization And Thermal Degradation Kinetics Of Cellulose Nanoparticles (CNPs) Production From Coconut Fiber

• Main Author: Tim, Mau Sean
• Format: Monograph
• Language: English
• Published: Universiti Sains Malaysia 2021
• Subjects: T Technology; TP Chemical Technology
• Online Access: http://eprints.usm.my/55121/1/Optimization%20And%20Thermal%20Degradation%20Kinetics%20Of%20Cellulose%20Nanoparticles%20%28CNPs%29%20Production%20From%20Coconut%20Fiber_Tim%20Mau%20Sean_K4_2021_ESAR.pdf; http://eprints.usm.my/55121/

Optimization and thermal degradation kinetics of the cellulose nanoparticles (CNPs) production from coconut fibers were presented in this research project. The research project was carried out by using the data collected from the thesis with the title of ‘Dissolution of Natural Fiber using Ionic Liquid for Production of Cellulose Nanoparticles’ by Nadzirah binti Yahya. For optimization part, the CNPs production from coconut fibers was simulated using the Response Surface Methodology (RSM) via Central Composite Design (CCD) in Design Expert software to determine the relationship between the affecting parameters (extraction time, extraction temperature and ratio of coconut fiber (CF) to ionic liquid (IL)) on the response (ultrafine CNPs distribution). The simulation results showed that both the extraction temperature and CF:IL ratio were more significant to the regression model. The maximum ultrafine CNPs distribution obtained through the optimization process was 58.074 %, under the operating conditions of 30 mins of extraction time, 69.518 °C of extraction temperature and 0.01 w/w of CF:IL ratio. For the thermal degradation kinetics study of CNPs produced from coconut fibers, both the model-free isoconversional Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods were used to determine the activation energy, Ea of the CNPs. However, the calculated results of Ea were not satisfied, which were ranged from -0.039 to -3.134 kJ/mol. The values deviated very much from the Ea value of common CNPs, and the possible source of error was predicted to be from the inaccurate results from the collected data.