This master's thesis investigates the mechanical properties of hydrogels based on alginate and TEMPO-oxidized nanocellulose (TONCF), along with their mathematical modeling. The aim of the research was to experimentally evaluate the effect of composition on elasticity, tensile strength, and network architecture of the composites, and to develop predictive models for mechanical response based on component ratios. Samples were prepared with varying alginate concentrations (5–10 wt%) and TONCF fractions (0–100%) at a constant CaCl₂ and TONCF concentration (3%).
Mechanical properties were assessed through tensile testing. The results demonstrated that the composites exhibit reinforcing effects on mechanical properties: both Young’s modulus and tensile strength surpassed the values of the individual components. Young’s modulus peaked at 75% TONCF, while tensile strength reached a maximum at 25% TONCF, confirming non-linear and property-specific behavior of the mixtures.
Three modeling approaches were developed to predict mechanical properties. The Random Forest model showed the highest accuracy, with R² = 0.9838 (Young’s modulus) and R² = 0.9888 (tensile strength), though it lacks interpretability and extrapolation capability. The extrapolation model enables prediction beyond the experimental composition range (for alginate), with slightly lower accuracy (R² ≈ 0.91–0.94). The third, semi-empirical model, based on percolation theory, offers a physically grounded interpretation of observed phenomena and explains changes in network density and interchain interactions.
The study demonstrates that combining experimental data with modeling enables effective prediction and explanation of the mechanical behavior of alginate–cellulose hydrogels, providing a solid foundation for further development of functional soft materials.
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