Towards Controlled Synthesis and Scalable Manufacturing of 2D Crystals

The emergence of two-dimensional (2D) materials has captured the imagination of researchers since graphene was first exfoliated from graphite in 2004. Their exotic properties give rise to many exciting potential applications in advanced electronic, optoelectronic, energy and biomedical technologies. Scalable growth of high quality 2D materials is crucial for their adoption in technological applications the same way the arrival of high-quality silicon single crystals was to the semiconductor industry. A huge amount of effort has been devoted to grow large-area, highly crystalline 2D crystals such as graphene and transition metal dichalcogenides (TMDs) through various methods. <br/>While CVD growth of wafer-scale monolayer graphene and TMDs has been demonstrated, considerable challenges still remain. In this talk, we first advocate for the focus on the crystal growth morphology as an underpinning for understanding, diagnosing and controlling the CVD process and environment for 2D material growth. Like snowflakes in nature, 2D crystals exhibit a rich variety of morphologies under different growth conditions. The mapping of crystal shapes in the growth parameter space “encodes” a wealth of information, the deciphering of which will lead to better understanding of the fundamental growth mechanism and materials properties. However, the morphology pattern evolution of 2D crystals such as MoS₂ monolayers under a practical CVD growth condition is highly complicated due to the entanglement of multiple growth factors. The ability to directly monitor it in real-time would be substantial to provide first-hand data to lay the groundwork for most advanced tools such as machine-learning to unravel those threads. A customized system with the function of observing and recording the CVD growth of MoS₂ in a miniature furnace is developed in this work. Image processing techniques are utilized to convert the real-time growth footage into frame-wise digital numbers and machine-learning is deployed to uncover the importance of multiple controlling factors in the growth. The model successfully guides the discovery of experimental control parameters to grow ultra-large size MoS₂monolayers. The model also demonstrates the possibility to trace back experimental condition by analyzing the crystal morphology parameters. In a parallel effort, we also demonstrate that the widely used powder-processing technique of dry ball-mill, can be improved to produce high-quality 2D flakes in large scale and with low cost. Seventeen types of commonly seen polymers, including both artificial and natural ones, have been examined as additive to dry ball-mill of hexagonal boron nitride. The potential of polymer-assisted ball-mill exfoliation as a universal way to produce ultra-thin 2D nanosheets is also demonstrated.