Multiscale micromechanical modeling for stiffness prediction of lignin-impregnated holocellulose composites

Abstract

In the context of climate change and global warming, the building industry, being one of the main contributors, is challenged to reduce its total emissions. This has led to an increased use of sustainable building materials, as evidenced by the growing popularity of timber construction. The effects of climate change and increased demand are putting pressure on European wood sources. In the wood product value chain, less than 50% of the raw material is processed intoparts for final constructions. Most of the raw material is used in low-value chains or burned. On the microscale, these by-products contain the same valuable structures as lumber, thereby enabling a substantial increase in sustainable building materials without the use of additional natural resources. Rather than allowing the stored carbon to be released as CO2 during the burning process, it is retained within the building’s structural framework. One method for leveraging the latent structures present in by-products such as sawmill residue involves the extraction of holocellulose fibers and lignin. Subsequently, these components arereassembled, with fibers serving as the primary load-bearing structure and lignin functioning as a binding agent. The objective of this study is to estimate the macroscopic mechanical performance of innovative biocomposite materials. This is accomplished through the utilization of a multi-scale micromechanics model, which allows the estimation of the mechanical properties based on the intrinsic properties of the constituents and key microstructural parameters. The model enables the calculation of specific fiber compositions and sample configurations, thereby reducing the time-consuming and costly nature of traditional testing methods. In a previous study, a broad set of paper sheets made from holocellulose and impregnated with various technical lignins were tested to assess their mechanical properties. These samples were used to support an initial calibration-based assessment. The model highlights the pivotal role of fiber orientations and fiber-fiber interactions in determining the mechanical performance of fiber-reinforced lignin-impregnated biocomposites. The model enables the estimation of stiffness for a range of sample configurations, depending upon the availability of critical input parameters such as component fractions, fiber orientations and the degree of fiber-fiber interaction. While unknown input values can be estimated by fittingthe model to testing results, this approach inherently limits the ability to validate the model’s predictive capabilities independently. Overall, the proposed model demonstrates the potential to generate accurate stiffness estimations; further validation is necessary to ensure its reliability. This validation should incorporate detailed knowledge of input values and additional testing results to enable comparison.