Characterization and Modelling of Nanomaterials Synthesized by Chemical Vapor Deposition

Abstract

In recent years, Chemical Vapor Deposition (CVD) has emerged as a pivotal technique for the synthesis of high-quality nanomaterials, owing to its ability to produce uniform and scalable thin films with controlled properties. This study presents a comprehensive characterization and modelling of nanomaterials synthesized via CVD, elucidating the intricate relationship between process parameters and the resultant material properties. Utilizing advanced characterization techniques, including Transmission Electron Microscopy (TEM), XPS, and Raman Spectroscopy, we have discerned the morphological, compositional, and structural attributes of the synthesized nanomaterials. The experimental data were subsequently employed to develop a predictive model, leveraging machine learning algorithms, to forecast the properties of nanomaterials based on CVD parameters. The model exhibited high accuracy and can serve as a robust tool for optimizing CVD processes in real-time. Our findings underscore the potential of CVD in tailoring nanomaterial properties for specific applications and provide a foundational framework for researchers and industries aiming to harness the full potential of nanomaterials synthesized via CVD. This work not only advances our understanding of CVD-synthesized nanomaterials but also paves the way for their application in next-generation electronic, photonic, and energy devices.