Real-Time Investigation of Sulfur Vacancy Generation and Passivation in Monolayer Molybdenum Disulfide via in situ X-ray Photoelectron Spectromicroscopy.

Understanding the chemical and electronic properties of point defects in two-dimensional materials, as well as their generation and passivation, is essential for the development of functional systems, spanning from next-generation optoelectronic devices to advanced catalysis. Here, we use synchrotron-based X-ray photoelectron spectroscopy (XPS) with submicron spatial resolution to create sulfur vacancies (SVs) in monolayer MoS2 and monitor their chemical and electronic properties in situ during the defect creation process. X-ray irradiation leads to the emergence of a distinct Mo 3d spectral feature associated with undercoordinated Mo atoms. Real-time analysis of the evolution of this feature, along with the decrease of S content, reveals predominant monosulfur vacancy generation at low doses and preferential disulfur vacancy generation at high doses. Formation of these defects leads to a shift of the Fermi level toward the valence band (VB) edge, introduction of electronic states within the VB, and formation of lateral pn junctions. These findings are consistent with theoretical predictions that SVs serve as deep acceptors and are not responsible for the ubiquitous n-type conductivity of MoS2. In addition, we find that these defects are metastable upon short-term exposure to ambient air. By contrast, in situ oxygen exposure during XPS measurements enables passivation of SVs, resulting in partial elimination of undercoordinated Mo sites and reduction of SV-related states near the VB edge. Correlative Raman spectroscopy and photoluminescence measurements confirm our findings of localized SV generation and passivation, thereby demonstrating the connection between chemical, structural, and optoelectronic properties of SVs in MoS2.

3D Deep Learning Enables Accurate Layer Mapping of 2D Materials.

Layered, two-dimensional (2D) materials are promising for next-generation photonics devices. Typically, the thickness of mechanically cleaved flakes and chemical vapor deposited thin films is distributed randomly over a large area, where accurate identification of atomic layer numbers is time-consuming. Hyperspectral imaging microscopy yields spectral information that can be used to distinguish the spectral differences of varying thickness specimens. However, its spatial resolution is relatively low due to the spectral imaging nature. In this work, we present a 3D deep learning solution called DALM (deep-learning-enabled atomic layer mapping) to merge hyperspectral reflection images (high spectral resolution) and RGB images (high spatial resolution) for the identification and segmentation of MoS2 flakes with mono-, bi-, tri-, and multilayer thicknesses. DALM is trained on a small set of labeled images, automatically predicts layer distributions and segments individual layers with high accuracy, and shows robustness to illumination and contrast variations. Further, we show its advantageous performance over the state-of-the-art model that is solely based on RGB microscope images. This AI-supported technique with high speed, spatial resolution, and accuracy allows for reliable computer-aided identification of atomically thin materials.