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 (∼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.