Machine learning strategy to improve impact strength for PP/cellulose composites via selection of biomass fillers

• Published in: Science and technology of advanced materials Vol. 25; no. 1; p. 2351356
• Main Authors: Nakayama, Koyuru, Sakakibara, Keita
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 31.12.2024; Taylor & Francis Group
• Subjects: cellulose nanofiber; Focus on Dr. Ariga 60th Anniversary: From Nanotechnology to Nanoarchitectonics Select: 60 Materials Informatics; impact energy; infrared spectroscopy; Lignocellulose; machine learning; polymer composite; polymer composite; cellulose nanofiber; impact energy; Lignocellulose; machine learning; infrared spectroscopy
• ISSN: 1468-6996; 1878-5514
• DOI: 10.1080/14686996.2024.2351356

Lignocellulosic materials have inherent complexities and natural nanoarchitectures, such as various chemical constituents in wood cell walls, structural factors such as fillers, surface properties, and variations in production. Recently, the development of lignocellulosic filler-reinforced polymer composites has attracted increasing attention due to their potential in various industries, which are recognized for environmental sustainability and impressive mechanical properties. The growing demand for these composites comes with increased complexity regarding their specifications. Conventional trial-and-error methods to achieve desired properties are time-intensive and costly, posing challenges to efficient production. Addressing these issues, our research employs a data-driven approach to streamline the development of lignocellulosic composites. In this study, we developed a machine learning (ML)-assisted prediction model for the impact energy of the lignocellulosic filler-reinforced polypropylene (PP) composites. Firstly, we focused on the influence of natural supramolecular structures in biomass fillers, where the Fourier transform infrared spectra and the specific surface area are used, on the mechanical properties of the PP composites. Subsequently, the effectiveness of the ML model was verified by selecting and preparing promising composites. This model demonstrated sufficient accuracy for predicting the impact energy of the PP composites. In essence, this approach streamlines selecting wood species, saving valuable time.