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| DOI | 10.1021/ACS.JPCC.2C00425 | ||||
| Año | 2022 | ||||
| Tipo | artículo de investigación |
Citas Totales
Autores Afiliación Chile
Instituciones Chile
% Participación
Internacional
Autores
Afiliación Extranjera
Instituciones
Extranjeras
The field of two-dimensional (2D) nanomaterials has gained significant interest over the last few decades in numerous applications because of their unique properties that exhibit when a bulk material is reduced to its 2D form. A wide variety of 2D layered materials are synthesized by a newly developed compressible flow exfoliation (CFE) process, which has considerable advantages over current top-down approaches. In this study, classical molecular dynamics (MD) simulations are used to investigate the interactions of gas particles with pristine, unfunctionalized graphene sheets during the CFE process and try to understand the atomistic mechanism of layer separation. The thermal vibration of graphene layers increases with the elevation of temperature that accelerates the exfoliation tendency, but the presence of static gas particles is insignificant here because of their lower binding energy. The range of one-directional flow velocities is incorporated to the compressible gases to replicate the experimental situation, and dispersion of graphene is observed when the velocity exceeds the supersonic flow condition. Analyzing the dynamic properties of exfoliation, it is established that sliding or the parallel direction is the preferable exfoliation mechanism of graphene than vertical separation. Besides, the upstream pressure plays a fundamental role because gas density and flow velocity are associated with that. It is also observed that heavier gas is less susceptible to delaminate graphene than lighter gas because of their higher atomic mass and lower flow rate at identical conditions. The findings of this study provide more flexibility to synthesize not only graphene but any 2D materials using compressible gases.
| Ord. | Autor | Género | Institución - País |
|---|---|---|---|
| 1 | Ahmed, Shafkat | - |
The University of Toledo - Estados Unidos
Univ Toledo - Estados Unidos College of Engineering - Estados Unidos |
| 2 | Arabha, Saeed | Hombre |
York University - Canadá
York Univ - Canadá Lassonde School of Engineering - Canadá |
| 3 | GONZALEZ-VALDES, RAFAEL IGNACIO | Hombre |
Universidad Mayor - Chile
Centro para el Desarrollo de la Nanociencia y la Nanotecnologia - Chile |
| 4 | Rizvi, Reza | Hombre |
The University of Toledo - Estados Unidos
York University - Canadá York Univ - Canadá Univ Toledo - Estados Unidos College of Engineering - Estados Unidos Lassonde School of Engineering - Canadá |
| Fuente |
|---|
| National Science Foundation |
| Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia |
| Natural Sciences and Engineering Research Council of Canada |
| Fondo Nacional de Investigaciones Cientificas y Tecnologicas (FONDECYT, Chile) |
| National Laboratory for High Performance Computing (Chile) |
| Fondo Nacional de Investigaciones Cientificas y Tecnologicas |
| National Laboratory for High Performance Computing ECM-02 |
| Canadian National Science and Engineering Research Council (NSERC) Discovery Grant Program |
| supercomputing infrastructure of the Ohio Supercomputer Center (USA) |
| Compute Canada.SharcNet (Canada) |
| US National Science Foundation's Nanomanufacturing Program (CMMI Award) |
| Agradecimiento |
|---|
| This work was supported by the US National Science Foundation’s Nanomanufacturing Program (CMMI Award # 1762507), as well as the Canadian National Science and Engineering Research Council (NSERC) Discovery Grant Program (Award # RGPIN-2019-06345). Furthermore, this work was supported by the Fondo Nacional de Investigaciones Cientificas y Tecnologicas (FONDECYT, Chile) #11180557 and Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia AFB180001. Computational resources for this research were partially supported by the supercomputing infrastructure of the Ohio Supercomputer Center (USA), Compute Canada─SharcNet (Canada), and National Laboratory for High Performance Computing─ECM-02 (Chile). |
| This work was supported by the US National Science Foundation’s Nanomanufacturing Program (CMMI Award # 1762507), as well as the Canadian National Science and Engineering Research Council (NSERC) Discovery Grant Program (Award # RGPIN-2019-06345). Furthermore, this work was supported by the Fondo Nacional de Investigaciones Cientificas y Tecnologicas (FONDECYT, Chile) #11180557 and Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia AFB180001. Computational resources for this research were partially supported by the supercomputing infrastructure of the Ohio Supercomputer Center (USA), Compute Canada─SharcNet (Canada), and National Laboratory for High Performance Computing─ECM-02 (Chile). |
| This work was supported by the US National Science Foundation's Nanomanufacturing Program (CMMI Award #1762507), as well as the Canadian National Science and Engineering Research Council (NSERC) Discovery Grant Program (Award #RGPIN-2019-06345). Furthermore, this work was supported by the Fondo Nacional de Investigaciones Cientificas y Tecnologicas (FONDECYT, Chile) #11180557 and Financiamiento Basal para Centros Cientificos y Tecnologicos de Excelencia AFB180001. Computational resources for this research were partially supported by the supercomputing infrastructure of the Ohio Supercomputer Center (USA), Compute Canada.SharcNet (Canada), and National Laboratory for High Performance Computing. ECM-02 (Chile). |