Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) Bionanocomposites with Crystalline Nanocellulose and Graphene Oxide: Experimental Results and Support Vector Machine Modeling

Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBHHx) is a biodegradable and biocompatible bacterial copolymer used in the biomedical and food industries. However, it displays low stiffness and strength for certain applications. This issue can be solved via reinforcement with nanofillers. In this work, PHBHHx-based bionanocomposites reinforced with different loadings of crystalline nanocellulose (CNC) and graphene oxide (GO) were developed by a green and straightforward solution casting technique. Their crystalline nature and surface topography were explored via X-ray diffraction (XRD) and field-emission scanning electron microscopy (FE-SEM), respectively, their composition was corroborated via Fourier-transformed infrared spectroscopy (FTIR), and their crystallization and melting behavior were determined via differential scanning calorimetry (DSC). The nanofillers had a nucleating role, raising the crystallization temperature of the polymer, whilst hardly any changes were found in the melting temperature. Further, significant enhancements in the stiffness, strength, and thermal stability of the PHBHHx matrix were observed with the incorporation of both nanofillers, which was attributed to a synergic effect. The mechanical properties for various concentrations of CNC and GO were accurately predicted using a machine learning (ML) model in the form of a support vector machine (SVM). The model performance was evaluated in terms of the mean absolute error (MAE), the mean square error (MSE), and the correlation coefficient (R2). These bio-based nanocomposites are a valuable alternative to conventional petroleum-based synthetic polymeric materials used nowadays for biomedicine and food packaging applications.

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Predicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV)-based bionanocomposites reinforced with graphene oxide (GO) and montmorillonite nanoclay were prepared herein via an environmentally friendly electrochemical process followed by solution casting. The aim was to evaluate the effectiveness of different Machine Learning (ML) models, namely Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM), in predicting their mechanical properties. The algorithms' input data were the Young's modulus, tensile strength, and elongation at break for various concentrations of the nanofillers (GO and nanoclay). The correlation coefficient (R2), mean absolute error (MAE), and mean square error (MSE) were used as statistical indicators to assess the performance of the models. The results demonstrated that ANN and SVM are useful for estimating the Young's modulus and elongation at break, with MSE values in the range of 0.64-1.0% and 0.14-0.28%, respectively. On the other hand, DT was more suitable for predicting the tensile strength, with the indicated error in the range of 0.02-9.11%. This study paves the way for the application of ML models as confident tools for predicting the mechanical properties of polymeric nanocomposites reinforced with different types of nanofiller, with a view to using them in practical applications such as biomedicine.

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system’s properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.