467512 Generalized Mechanistic Model for the Chemical Vapor Deposition of 2D Transition Metal Dichalcogenide Monolayers

Wednesday, November 16, 2016: 1:56 PM
Golden Gate 5 (Hilton San Francisco Union Square)
Ananth Govind Rajan1, Jamie H. Warner2, Daniel Blankschtein1 and Michael S. Strano1, (1)Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, (2)Department of Materials, University of Oxford, Oxford, United Kingdom

Transition metal dichalcogenides (TMDs) like molybdenum disulfide (MoS2) and tungsten disulfide (WS2) are layered materials capable of growth to one monolayer thickness via chemical vapor deposition (CVD). Such CVD methods, while powerful, are notoriously difficult to extend across different reactor types and conditions, with subtle variations often confounding reproducibility, particularly for 2D TMD growth. In this work, we formulate the first generalized TMD synthetic theory by constructing a thermodynamic and kinetic growth mechanism linked to CVD reactor parameters that is predictive of specific geometric shape, size, and aspect ratio from triangular to hexagonal growth, depending on specific CVD reactor conditions. We validate our model using experimental data from Wang et al. (Chem. Mater., 2014, 26 (22), 6371-6379) that demonstrate the systemic evolution of MoS2 morphology down the length of a flow CVD reactor where variations in gas phase concentrations can be accurately estimated using a transport model (CSulfur = 9–965 μmol/m3; CMoO3 = 15–16 mmol/m3) under otherwise isothermal conditions (700 oC). A stochastic model which utilizes a site-dependent activation energy barrier based on the intrinsic TMD bond energies and a series of Evans-Polanyi relations leads to remarkable, quantitative agreement with both shape and size evolution along the reactor. The model is shown to extend to the growth of WS2 at 800 oC and MoS2 under varied process conditions. Finally, a simplified theory is developed to translate the model into a “kinetic phase diagram” of the growth process. The predictive capability of this model and its extension to other TMD systems promise to significantly increase the controlled synthesis of such materials.

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