Description
Microwave plasmas sustained at atmospheric pressure generated by a TIAGO (Torche à Injection Axiale sur Guide d’Ondes) have demonstrated significant potential for production of high-quality, low-cost graphene from ethanol, achieving production rates of 110 mg/h. Despite the process being highly efficient and ecologically sustainable, key parameters such as reactor geometry, precursor injection methods and reactor construction materials remain unoptimized for graphene production. In this context, modelling the plasma behavior within a TIAGO reactor emerges as a powerful approach to explore a wide range of operating conditions without the need to manufacture multiple intermediate prototypes or to conduct many experiments, thereby reducing both time and cost.
To address the challenges mentioned above, a two-temperature fluid-electromagnetic computational model for non-thermal microwave plasmas at atmospheric pressure has been developed (MoTiTo, MOdelling of TIago Torch). MoTiTo is implemented within ANSYS Fluent© resulting into a self-consistent model that couples Navier–Stokes equations, electromagnetic field, energy conservation for ions and electrons, k-epsilon turbulence model and the transport of chemical species, offering quick convergence and robustness which is often difficult in this kind of models. The model has been validated with an experimental case of Argon plasma at different operating conditions of flow rate (1-5 slpm) and microwave power (350-600 W). Modeling results show good agreement with the experimental case for gas temperature, electron temperature and electron density. The model also provides information about other fields that are experimentally difficult to measure, such as flow patterns, turbulence, chemical species density, or flow residence times, among others.
MoTiTo can be applied to test arbitrary reactor geometries under a wide range of boundary conditions, enabling the reproduction of different experimental configurations and other microwave plasma sources, such as surfatrons. Future steps involve the implementation of a nucleation model adapted to physicochemical processes related to graphene synthesis. This work provides a simulation tool to the plasma community and to engineers towards the understanding and optimization of nanomaterials synthesis conditions.
Acknowledgements: This work was supported by project PID2023-147436OA-I00 funded by MICIU/AEI /10.13039/501100011033 and by FEDER, UE, and by the University of Córdoba with the Plan Propio, UCOlidera 2024 Ref. DU.22.5B.24.01.