Optimizing the Synthesis of Polymer Nanocomposites with Bio-based Fillers Through Machine Learning Techniques

EasyChair Preprint 14504

Abstract

The integration of bio-based fillers into polymer nanocomposites presents a sustainable approach to enhancing material properties while reducing environmental impact. However, optimizing the synthesis process of these composites is complex due to the vast number of variables involved, including filler concentration, dispersion quality, and polymer-filler interactions. This study explores the application of machine learning (ML) techniques to optimize the synthesis of polymer nanocomposites with bio-based fillers. By leveraging ML algorithms, we systematically analyze experimental data to identify optimal processing conditions that maximize mechanical, thermal, and barrier properties. The study demonstrates how predictive models can efficiently navigate the high-dimensional parameter space, reducing the need for extensive trial-and-error experiments. The findings highlight the potential of ML-driven approaches in advancing the development of high-performance, eco-friendly polymer nanocomposites, paving the way for their broader adoption in various industries.

Keyphrases: Machine Learning (ML), Material optimization, predictive modeling

@booklet{EasyChair:14504,
  author    = {Abey Litty},
  title     = {Optimizing the Synthesis of Polymer Nanocomposites with Bio-based Fillers Through Machine Learning Techniques},
  howpublished = {EasyChair Preprint 14504},
  year      = {EasyChair, 2024}}