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

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.

This paper introduces a data-driven method to efficiently design lignocellulosic polymer composites with high-impact energy, optimizing components and surface areas using infrared spectroscopic data.

Modification of xylan via an oxidation-reduction reaction.

Xylan is a biopolymer readily available from forest resources. Various modification methods, including oxidation with sodium periodate, have been shown to facilitate the engineering applications of xylan. However, modification procedures are often optimized for semicrystalline high molecular weight polysaccharide cellulose rather than for lower molecular weight and amorphous polysaccharide xylan. This paper elucidates the procedure for the periodate oxidation of xylan into dialdehyde xylan and its further reduction into a dialcohol form and is focused on the modification work up. The oxidation-reduction reaction decreased the molecular weight of xylan while increased the dispersity more than 50%. Unlike the unmodified xylan, all the modified grades could be solubilized in water, which we see essential for facilitating the future engineering applications of xylan. The selection of quenching and purification procedures and pH-adjustment of the reduction step had no significant effect on the degree of oxidation, molecular weight and only a minor effect on the hydrodynamic radius in water. Hence, it is possible to choose the simplest oxidation-reduction route without time consuming purification steps within the sequence.