The control of layer thickness and phase structure in two-dimensional transition metal dichalcogenides (2D TMDCs) like MoTe 2 has recently gained much attention due to their broad applications in nanoelectronics and nanophotonics. Continuous-wave laser-based thermal treatment has been demonstrated to realize layer thinning and phase engineering in MoTe 2 , but requires long heating time and is largely influenced by the thermal dissipation of the substrate. The ultrafast laser produces a different response but is yet to be explored. In this work, we report the nonlinear optical interactions between MoTe 2 crystals and femtosecond (fs) laser, where we have realized the nonlinear optical characterization, precise layer thinning, and phase transition in MoTe 2 using a single fs laser platform. By using the fs laser with a low fluence as an excitation light source, we observe the strong nonlinear optical signals of second-harmonic generation and four-wave mixing in MoTe 2 , which can be used to identify the odd−even layers and layer numbers, respectively. With increasing the laser fluence to the ablation threshold (F th ), we achieve layer-by-layer removal of MoTe 2 , while 2H-to-1T′ phase transition occurs with a higher laser fluence (2F th to 3F th ). Moreover, we obtain highly ordered subwavelength nanoripples on both the thick and few-layer MoTe 2 with a controlled fluence, which can be attributed to the fs laser-induced reorganization of the molten plasma. Our study provides a simple and efficient ultrafast laser-based approach capable of characterizing the structures and modifying the physical properties of 2D TMDCs.

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Student evaluations of teaching (SET) have become a popular approach to assess faculties’ teaching. Question‐score‐based questionnaire is the most common SET measure adopted in universities. However, it fails to cover important facets of teaching process that not mentioned in the predefined questionnaire, which can be substantially obtained from students’ short reviews. In this paper, we propose two lexical‐based methods, specifically knowledge‐based and machine learning‐based, to automatically extract opinions from short reviews. Furthermore, the diversity of reviews’ themes and styles of same sentiment polarity reviews can be observed from the extracted opinion results. The experimental results show that the proposed methods are able to achieve accuracies of 78.13 and 84.78%, respectively in the task of student review sentiment classification. Further investigation on linguistic features shows that reviews with same sentiment polarity shares similar language patterns. Finally, we present an application scenario in real SET process by utilizing aforementioned methods and discoveries.

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