Authors: Elizabeth Champa-Bujaico | Ana M. Díez‐Pascual | Pilar García-Díaz | Valentina Sessini | Marta E. G. Mosquera
Source: Industrial Crops and Products (article, 2024-06-25)
Field: Materials Science — Polymers and Plastics

Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes Poly[(butylene succinate)-co-adipate] (PBSA)-based materials are gathering much attention in the packaging industry, agriculture, and other fields owed to their biocompatibility and biodegradability. Nonetheless, poor thermal and mechanical properties of biodegradable polymers, such as PBSA, have hampered their wide-spread use. Herein, a simple, cost-effective and scalable solution to improve the mechanical properties of PBSA is reported by using functionalized single-walled carbon nanotubes (SWCNTs). Different SWCNT loadings have been incorporated in the PBSA matrix via simple solution casting, and the ultrasonication conditions have been optimized to attain a homogenous SWCNT dispersion. The nanocomposites have been characterized in detail by scanning electron microscopy (SEM), Infrared spectroscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), tensile and impact strength tests. Unprecedented increments in stiffness were found at low nanotube loadings, ascribed to the outstanding reinforcing capability of the SWCNTs combined with their superior modulus and strong interfacial adhesion with the matrix via H-bonding, polar and CH-π interactions. Further, four machine learning (ML) algorithms, Polynomial Regression (PR), Support Vector Machines for Regression (SVR), Gradient Boosting (GB) and Artificial Neural Network (ANN), were applied to predict their mechanical properties. The algorithm´s performance was assessed using analytical parameters such as the coefficient of determination (R2), the mean square error (MSE) and the mean absolute error (MAE). The developed models exhibited strong performance, achieving R2 values ranging from 0.69 to 0.99 across the evaluated properties. The results corroborate that even when the same prediction model is used, its performance varies depending on the physical property to be predicted. Thus, SVR, GB, PR, and ANN were found to be the most effective for estimating the Young’s modulus, tensile strength, elongation at break and impact strength, respectively. This research holds great potential for advancing the field of modelling the mechanical properties of polymeric nanocomposites and their practical applications in various industries such as food, pharmaceutical and biomedicine. The development of accurate models for predicting nanocomposite properties would cheapen, simplify and systematize their design and production processes, resulting in improved final products and more efficient development procedures. Materials science ; Carbon nanotube ; Differential scanning calorimetry ; Nanocomposite ; Ultimate tensile strength ; Thermogravimetric analysis ; Coefficient of determination ; Composite material ; Algorithm ; Chemical engineering ; Machine learning ; Mathematics ; Computer science ; Thermodynamics Natural Fiber Reinforced Composites ; Carbon Nanotubes in Composites ; Additive Manufacturing and 3D Printing Technologies