Polycaprolactone synthesis via enzymatic catalysis using response surface methodology
Harshini Pakalapati1, Senthil Kumar Arumugasamy1*, Mohammad Khalid2, Jega Lakshmi3, Jacky Wong3
1Department of Chemical and Environmental Engineering, Faculty of Engineering,
University of Nottingham Malaysia Campus,
Jalan Broga, 43500 Semenyih, Selangor, Malaysia
2Research Centre for Nano-materials & Energy Technology, School of Science & Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Subang
Jaya, Selangor, Malaysia
3Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
*Corresponding author Tel: +60 3 8725 3627
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PREFERRED: Oral presentation
Full paper for Journal Publication.
Abstract:
Polycaprolactone (PCL) a biodegradable polymer is finding its increasing demand in today’s industry due to its rich properties and varied applications. Among various methods of PCL synthesis, enzymatic synthesis is preferred due to their eco-friendliness. However, the information on the optimization of parameters to achieve high molecular weight PCL is still scarce. Complete knowledge about process parameters and their interaction is important to obtain desired quality of polymer. This helps to extend its commercial application and scale of production. Till date, single factor optimization was only carried out in enzyme catalysed polycaprolactone synthesis. Therefore, in this study D optimal design, statistical approach is considered to optimize the process parameters. Temperature (50 - 110°C), Time (1-7 hrs), mixing speed (50 to 500 rpm) and monomer/solvent ratio (1:1 to 1:6) are considered. Molecular weight of the polymer measures the quality and also regulates other properties (thermal and mechanical) of the polymer. Hence, molecular weight was chosen as response and was analysed using matrix-assisted laser desorption/ionization time of flight (MALDI TOF). Further, the PCL is synthesized using optimised conditions to validate the predicted values. The results show a good agreement with a minimum error between the actual and predicted values. Using the D optimal method in design of experiments, the interactions of the parameters were studied. The regression model was validated with ANOVA and diagnostic plots.
Key words: Polycaprolactone; Enzymatic polymerization; Candida Antarctica lipase B; D optimal method; Optimization