Evaluating and Enhancing Liquid-Phase Exfoliation of Graphitic Carbon Nitride with Molecular Dynamics and Machine Learning
Creators: Shahini, Ehsan
Publisher: name: University of Alberta Library
Year: 2024

<p>Graphitic carbon nitride (g-C3N4) has gained significant attention due to its versatile applications in photocatalysis, energy conversion, and environmental remediation. To harness its full potential, high-quality g-C3N4 nanosheets are essential, which can be achieved through liquid-phase exfoliation (LPE). However, the efficiency of LPE is critically influenced by the choice of solvent, and understanding the molecular interactions involved remains a challenge. This thesis investigates the LPE of g-C3N4 using molecular dynamics (MD) simulations and machine learning (ML) techniques, with the aim to provide a comprehensive understanding on how the solvent selection and nanosheet functionalization impact exfoliation efficiency.<br>
First, MD simulations were employed to evaluate the free energy of exfoliation (ΔGexf) for g-C3N4 in various solvents. The findings revealed that solvents with higher magnitude of solvation free energy (ΔGsol) tend to facilitate exfoliation by forming stable adsorption layers around the nanosheets. To measure the stability, a quantity called solvent mobility was introduced, which benefited subsequent investigations in this thesis. Building on these insights, an ML model was developed to predict ΔGexf and ΔGsol for a wider range of solvents, significantly reducing the need for extensive MD simulations. The ML model identified several promising solvents, including benzyl alcohol (Bn), and methanesulfonic acid (MSA) which were experimentally validated to enhance the dispersibility of g-C3N4.<br>
The thesis further explored the use of binary solvent mixtures, uncovering the phenomenon of solvent dominance where one component governs the LPE performance. Detailed MD simulations demonstrated that in N-Methyl-2-Pyrrolidone (NMP):Cyclohexane mixture, the performance was dominated by NMP, leading to superior exfoliation efficiency. Conversely, in methanol:dichloromethane mixtures, methanol dominated, resulting in poor LPE performance. These findings challenge the traditional surface tension-based criteria for solvent selection, highlighting the need to consider molecular interactions in in the vicinity of the nanosheets. The solvent dominance motivated a novel strategy for designing binary solvent mixture with balanced LPE performance, cost and environmental friendliness.<br>
Finally, the impact of chemical functionalization on LPE efficiency was examined. Functional groups such as SO3H and COOH were found to significantly enhance exfoliation by reducing ΔGexf and promoting stable solvent-sheet interactions. The SO3H group, in particular, exhibited the most substantial enhancement due to strong dipole-dipole interactions, decreasing solvent mobility and increasing interaction strength around the nanosheets. NH2 and OH groups also contributed positively, though to a lesser extent, while CHO hindered the process by increasing ΔGexf and disrupting solvent-solvent interactions. These varied outcomes underscore the complexity of solvent-functional group interactions and provide practical guidelines for selecting effective solvents for functionalized g-C3N4.<br>
Overall, this dissertation advances the understanding of molecular mechanisms in LPE of g-C3N4, providing practical guidelines for solvent selection and functionalization to optimize nanosheet production. The integration of MD simulations and ML models offers a powerful framework for predicting and enhancing the exfoliation process, paving the way for scalable and efficient production of g-C3N4 for various technological applications.</p>