Ramezani, Ghazaleh (2026) Machine Learning-Enhanced Sensing Capability of Graphene Oxide and Reduced Graphene Oxide Embedded in a Nano-cellulose Matrix. PhD thesis, Concordia University.
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Abstract
This thesis addresses the critical need for sustainable, high-performance materials by exploring eco-friendly nanocomposites composed of graphene oxide (GO) and reduced graphene oxide (rGO) within a nanocellulose matrix. Nanocellulose, in the form of cellulose nanocrystals and nanofibers, is preferred due to its nanoscale dimensions that provide a high surface area, enhanced flexibility, and superior mechanical and chemical reactivity, making it an ideal matrix for integrating functional nanomaterials. GO and rGO complement each other: GO’s oxygen-containing groups facilitate surface modification and dispersion in aqueous media, while rGO restores electrical conductivity necessary for biosensing and flexible electronics. In this work, GO and rGO were synthesized and reduced using green agents like citric and L-ascorbic acids, offering scalable and safer alternatives to conventional methods.
Experimental studies on nanocellulose/GO and nanocellulose/rGO films demonstrated exceptional electrical conductivity, mechanical strength, and environmental stability, vital for wearable electronics and biosensors. Spectroscopic, microscopic, and electrochemical analyses revealed the roles of hydrogen bonding, π–π interactions, and composite architecture in performance. Predictive modeling using Lasso regression and neural networks established robust composition–property relationships with high accuracy (R² > 0.99).
Overall, this research confirms that nanocellulose composites with GO and rGO are renewable, recyclable, and functional under ambient conditions, presenting promising applications in biosensing, smart packaging, and health monitoring. The study advances our understanding of why nanocellulose and GO/rGO-based hybrids are uniquely suited, fostering progress in sustainable functional materials at the nanoscale.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering |
|---|---|
| Item Type: | Thesis (PhD) |
| Authors: | Ramezani, Ghazaleh |
| Institution: | Concordia University |
| Degree Name: | Ph. D. |
| Program: | Mechanical Engineering |
| Date: | 2026 |
| Thesis Supervisor(s): | Stiharu, Ion and van de Ven, Theo G.M. |
| ID Code: | 996927 |
| Deposited By: | Ghazaleh Ramezani |
| Deposited On: | 29 Jun 2026 17:59 |
| Last Modified: | 29 Jun 2026 17:59 |
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