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Unlocking nanocellulose production through the implementation of real-time monitoring strategies

Year: 2025
Type: dissertation
Abstract: ENG- The work first investigates the TEMPO-mediated oxidation of bleached kraft pulps, establishing the influence of reaction temperature, reagent concentration, and fiber surface area on oxidation kinetics. A strong linear relationship between NaOH consumption and carboxyl content was demonstrated, offering a practical strategy for real-time monitoring and precise reaction control. Complementary to this, portable near-infrared (NIR) spectroscopy coupled with multivariate calibration was shown to accurately predict the extent of oxidation across different pulps. By integrating PCA- and LDA-based classification of fiber types with pulp-specific regression models, the study proposes a versatile approach to overcome feedstock variability in monitoring strategies. Building on this, the research explores how the inherent chemical and supramolecular features of eucalyptus, pine, hemp, and sisal pulps govern both oxidation efficiency and the structural, rheological, and morphological properties of the resulting CNMs. The results demonstrate that wood-derived CNMs favor entangled fibrillar networks, while non-wood pulps yield more fragmented nanostructures, underscoring the need for tailored processing strategies depending on material origin. In parallel, enzymatic pretreatment using endoglucanase was examined as a sustainable alternative to chemical oxidation. Fiber-level changes were tracked using polarized light microscopy, while reducing sugar release was monitored as a proxy for enzymatic activity. Machine learning tools, particularly random forest regression, were developed to predict sugar concentration from measurable parameters such as fines content, apparent viscosity, and stirring power consumption. This novel approach minimizes reliance on offline chemical assays and opens pathways for agile process control. Together, these contributions provide an integrated framework for CNF production that couples mechanistic insights with real-time monitoring and predictive modeling. By demonstrating scalable approaches for both chemical and enzymatic pretreatments, this thesis bridges the gap between fundamental cellulose science and industrial implementation, highlighting how control over reaction kinetics, feedstock variability, and monitoring strategies can accelerate the deployment of nanocellulose as a sustainable material of the future
Language: English
Subfield: Biomaterials
Open Access status: green
OpenAlex ID: W7128877097
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