Kamble, Anuradha Mohan M

English

Polymer nanocomposites (PNCs) have gained significant attention in industrial applications due to their enhanced properties compared to traditional composites. These composites incorporate nano-sized particles into a polymer matrix, resulting in improved specific strength properties, weight savings, lightness, and design flexibility. They find applications in various industries, including aerospace, automotive, packaging, and electronics. However, optimizing the properties of PNCs requires a thorough understanding of nanoparticle synthesis, characterization, polymer matrix selection, and nanoparticle dispersion. In addition to experimental characterisation technics e.g. transmission electron microscopy and various spectroscopy technics, modelling and simulation methods, e.g. molecular dynamics (MD) simulations, have become indispensable tools in materials science research. However, MD simulations are limited by computing power and simulation time scale, making it challenging to study long-term processes or large systems.In recent years, there had been a notable rise in data-driven methods such as Deep Learning (DL), Machine Learning (ML), and Artificial Intelligence (AI), and within the field of materials science research. These methods have been applied to analyse experimental and simulation data, enabling the prediction of material properties, classification of microstructures, and identification of processing conditions yet the application of these methods to polymer nanocomposites is still limited, presenting an opportunity for further exploration.This thesis aims to explore the application of DL techniques to study the phase-separated microstructure of a novel nano-modified polymer composite. The research question focuses on the suitability of DL techniques for this purpose. The objectives include conducting a comprehensive literature review, collecting relevant data on the nano-modified polymer composite, identifying suitable research areas for DL, formulating a problem statement, and investigating the applications of DL techniques for the dataset.By leveraging DL models, this research aims to provide insights into the phase-separated microstructure of the nano-modified polymer composite, enabling the optimization of its properties. The findings from this study can be useful for future advancements in polymer nanocomposites.<br/

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Exploiting the deep learning technique to study a novel nano- modified polymer composite

https://research-information.bris.ac.uk/files/381409624/Final_Copy_2023_11_04_Kamble_Anuradha_MScR.pdf

Exploiting the deep learning technique to study a novel nano- modified polymer composite

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