Parametric optimization of polycaprolactone synthesis catalysed by Candida antarctica lipase B using response surface...

Area of Science:

• Polymer Chemistry
• Materials Science
• Statistical Modeling

Background:

• Polycaprolactone (PCL) is a versatile biodegradable polyester with numerous applications.
• Optimizing PCL synthesis is crucial for controlling its molecular weight and material properties.
• Existing methods may lack efficiency in parameter optimization.

Purpose of the Study:

• To optimize process parameters for polycaprolactone (PCL) synthesis.
• To identify optimal conditions for achieving desired molecular weight.
• To validate the predictive accuracy of the statistical model.

Main Methods:

• Employed a D-optimal design for statistical process optimization.
• Investigated parameters including temperature (50-110°C), time (1-7h), mixing speed (50-500rpm), and monomer/solvent ratio (1:1-1:6).
• Determined molecular weight using matrix-assisted laser desorption/ionization time of flight (MALDI-TOF).

Main Results:

• The D-optimal design effectively analyzed interactions between process parameters and molecular weight.
• Validated results showed good agreement between actual and predicted molecular weights.
• Achieved a minimum error, indicating high predictive accuracy of the model.

Conclusions:

• The D-optimal statistical approach is a robust method for optimizing PCL synthesis.
• This method allows for precise control over PCL molecular weight by tuning key process variables.
• The validated model provides a reliable tool for predicting and achieving target molecular weights in PCL production.