Correlative SEM-AFM Characterization of 2D Materials: From Sequential Measurements to In-Situ Multimodal Platforms.

With atomic-scale thickness and defect-sensitive structures, stimuli-responsive two-dimensional (2D) materials exhibit diversified and highly localized physicochemical behaviors under external triggers such as electric fields, illumination, mechanical strain and chemical environments. These behaviors are intricately coupled with layer variations, grain boundaries, folds and local strain fields, which makes it necessary to reveal the structure-performance relationships of stimuli-responsive 2D materials. Compared with conventional single-technique characterization, the combination of scanning electron microscopy (SEM) and atomic force microscopy (AFM) provides a powerful framework for correlating structural, morphological and functional information across length scales. In this review, a systematic overview of correlative and integrated SEM-AFM characterization of 2D materials is given, covering its origins, working principles, typical applications and recent progress. Representative measurement configurations are classified into three categories-sequential SEM and AFM characterization, integrated AFM-in-SEM platforms, and truly in-situ or simultaneous SEM-AFM measurements-and the corresponding applications to stimuli-responsive 2D materials are discussed with respect to electrical, optical, mechanical and multiphysics characterization. The experimental challenges and limitations of SEM-AFM integration, including electron-beam effects, contamination, vacuum constraints and drift-related issues, are critically analyzed. Finally, the development of intelligent SEM-AFM platforms accompanied by emerging deep learning and artificial intelligence (AI) paradigms is outlined as a perspective, with emphasis on multimodal data fusion and automated feature extraction.