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Abstract：
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.
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.
摘要：
木质纤维素材料具有固有的复杂性和天然的纳米结构，如木材细胞壁中的各种化学成分，结构因素，如填料，表面属性,和生产的变化。最近,木质纤维素填料增强聚合物复合材料的发展由于其在各个行业的潜力而引起了越来越多的关注，这是公认的环境可持续性和令人印象深刻的机械性能。对这些复合材料日益增长的需求伴随着其规格的复杂性增加。实现所需性能的常规试错方法是耗时且昂贵的，对高效生产构成挑战。解决这些问题,我们的研究采用数据驱动的方法来简化木质纤维素复合材料的开发。在这项研究中,我们开发了一种机器学习（ML）辅助预测模型，用于木质纤维素填料增强聚丙烯（PP）复合材料的冲击能。首先,我们专注于生物质填料中天然超分子结构的影响，其中使用傅立叶变换红外光谱和比表面积，研究了PP复合材料的力学性能。随后，通过选择和制备有前景的复合材料，验证了ML模型的有效性。该模型证明了预测PP复合材料冲击能量的足够准确性。实质上,这种方法简化了木材种类的选择，节省宝贵的时间。
本文介绍了一种数据驱动的方法，可有效地设计具有高冲击能量的木质纤维素聚合物复合材料，使用红外光谱数据优化组件和表面积。
本文介绍了一种数据驱动的方法，可有效地设计具有高冲击能量的木质纤维素聚合物复合材料，使用红外光谱数据优化组件和表面积。
