Visualized and Nondestructive Quality Identification of Two-Dimensional MoS2 Based on Principal Component Analysis.

Abstract

To date, the common quality characterizations for MoS2 are inefficient or cause irreversible damage to the samples, which have limited scalability and low throughput. Here, we propose a visualized and nondestructive approach to evaluate the quality of MoS2 based on the PCA machine learning method. Through PCA processing of PL mapping, the CVD grown MoS2 with different edge defect densities can be well distinguished. Furthermore, six twin GBs along the sulfur zigzag direction of the six pointed MoS2 stars are also successfully identified. To verify the correctness of the identification results, we measured the lifetime mapping and thermal expansion coefficient of the synthesized MoS2 samples. It is found that the high quality MoS2 samples have a shorter carrier lifetime (∼0.291 ns) and lower thermal expansion coefficient (∼2.03 × 10-5K-1). Therefore, our work offers a new approach to evaluate the quality of MoS2 to drive their practical application.