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

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 (similar to 0.291 ns) and lower thermal expansion coefficient (similar to 2.03 x 10(-5)K(-1)). Therefore, our work offers a new approach to evaluate the quality of MoS2 to drive their practical application.

Automated summary

To date, evaluating the quality of MoS2 has been inefficient or caused irreversible damage to samples, limiting scalability and throughput. In this study, we propose a visualized and nondestructive approach using the PCA machine learning method to evaluate MoS2 quality. By applying PCA processing to PL mapping, we successfully distinguish CVD-grown MoS2 samples with different edge defect densities and identify six twin GBs in the MoS2 stars. The verification of identification results through lifetime mapping and thermal expansion coefficient measurements further supports the effectiveness of our approach in assessing MoS2 quality.