Utilizing Bio-Based Materials in Polymer Nanocomposites: a Machine Learning Approach for Material Design

EasyChair Preprint 14502

Abstract

The integration of bio-based materials into polymer nanocomposites represents a promising avenue for developing sustainable and environmentally friendly materials with enhanced mechanical properties. This study explores the potential of utilizing bio-based fillers, such as cellulose nanocrystals, lignin, and chitosan, in polymer matrices to create high-performance nanocomposites. A machine learning approach is employed to optimize the material design process, predicting the mechanical properties of these nanocomposites based on the characteristics of the bio-based fillers and polymer matrices. By leveraging large datasets and advanced algorithms, the study identifies optimal compositions and processing conditions that maximize the mechanical performance, such as tensile strength, elasticity, and impact resistance, while minimizing environmental impact. The findings demonstrate the efficacy of machine learning in accelerating the development of bio-based polymer nanocomposites, offering a pathway towards more sustainable material solutions in various industrial applications.

Keyphrases: Bio-based materials, machine learning, polymer nanocomposites

@booklet{EasyChair:14502,
  author    = {Abey Litty},
  title     = {Utilizing Bio-Based Materials in Polymer Nanocomposites: a Machine Learning Approach for Material Design},
  howpublished = {EasyChair Preprint 14502},
  year      = {EasyChair, 2024}}