Modeling and elaboration of multifunctional and ecofriendly nanocomposite materials based on polyaniline and functionalized graphene

Abstract

This PhD thesis presents a comprehensive exploration of the development andoptimization of polyaniline (PANI) and functionalized graphene nanocomposites, a pivotal advancement in materials science and engineering. Emphasizing a multidisciplinary approach, this work integrates innovative experimental strategies, computational modeling, and advanced machine learning techniques to refine the properties of these nanocomposites. The research encapsulates innovative synthesis methods and employs groundbreaking machine learning models for predictive analysis, significantly enhancing the accuracy in forecasting electrical conductivity and gas-sensing responses of PANI/graphene nanocomposites. These models, a first in the field, set new standards in sustainable material science. Furthermore, the thesis introduces an eco-friendly synthesis pathway for PANI/reduced graphene oxide nanocomposites using deep eutectic solvents, marking a leap towards environmental sustainability. This integrative research not only enriches the understanding of PANI/graphene nanocomposites but also makes substantial contributions to developing sustainable materials. The findings and methodologies presented in this thesis are anticipated to have far-reaching impacts, opening new avenues in technology and environmental conservation, particularly in areas such as supercapacitors, gas sensors, and energy storage solutions.