Optimization of Bio-Based Polymer Nanocomposites Synthesis through Artificial Intelligence and Machine Learning Approaches

EasyChair Preprint 14517

Abstract

The quest for sustainable materials has intensified interest in bio-based polymer nanocomposites due to their potential for reduced environmental impact and enhanced mechanical properties. This paper explores the optimization of bio-based polymer nanocomposites synthesis using artificial intelligence (AI) and machine learning (ML) techniques. We propose a framework that integrates advanced AI algorithms and ML models to streamline the synthesis process, optimize formulation parameters, and predict material performance. By leveraging AI-driven optimization tools and predictive models, we can enhance the design and processing of nanocomposites, leading to improved mechanical properties, durability, and functionality. Our approach involves the use of genetic algorithms, neural networks, and data-driven methodologies to analyze and refine synthesis parameters, such as polymer matrix composition, filler concentration, and processing conditions. The results demonstrate significant improvements in material performance and processing efficiency, showcasing the potential of AI and ML in advancing the field of bio-based polymer nanocomposites. This study provides a comprehensive overview of the integration of AI and ML in material science and highlights future directions for research and development in sustainable materials.

Keyphrases: Artificial Intelligence, Bio-based polymers, Nanocomposites

@booklet{EasyChair:14517,
  author    = {Abey Litty},
  title     = {Optimization of Bio-Based Polymer Nanocomposites Synthesis through Artificial Intelligence and Machine Learning Approaches},
  howpublished = {EasyChair Preprint 14517},
  year      = {EasyChair, 2024}}