Enzymatic pretreatment for cellulose nanofiber production: Understanding morphological changes and predicting...

Area of Science:

• Biotechnology
• Materials Science
• Chemical Engineering

Background:

• Enzymatic pretreatment is vital for cellulose nanofiber (CNF) production.
• Current methods lack real-time monitoring and clear links between enzymatic action and CNF properties.

Purpose of the Study:

• To evaluate enzyme charge and treatment time effects on cellulose fiber morphology and sugar release.
• To develop a predictive model for enzymatic pretreatment using machine learning.
• To understand the relationship between enzymatic pretreatment and resulting CNF characteristics.

Main Methods:

• Enzymatic treatment of cellulose fibers with endoglucanase.
• Monitoring fiber morphology using polarized light optical microscopy.
• Quantifying reducing sugar generation.
• Developing a random forest model using process parameters and fiber characteristics.
• Assessing CNF yield and cationic demand.

Main Results:

• Endoglucanase treatment initially causes surface fibrillation, followed by fiber swelling and shortening.
• A random forest model accurately predicts reducing sugar concentration from measurable parameters.
• High enzyme charge and short treatment times (90 min) yield 19-23% CNF with 220-275 μeq/g cationic demand.

Conclusions:

• Enzymatic pretreatment can be effectively modeled and optimized for CNF production.
• This approach reduces uncertainties for industrial upscaling of CNF manufacturing.
• The study provides a framework for real-time monitoring and control of enzymatic pretreatment processes.