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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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56 records · Page 4

Scanning probe spectroscopy of sulfur vacancies and MoS 2 monolayers in side-contacted van der Waals heterostructures

We investigate the interplay between vertical tunneling and lateral transport phenomena in electrically contacted van der Waals heterostructures made from monolayer MoS 2 , hBN, and graphene. We compare data taken by low-temperature scanning tunneling spectroscopy to results from room-temperature conductive atomic force spectroscopy on monolayer MoS 2 with sulfur vacancies and with varying hBN layers. We show that for thick hBN barrier layers, where tunneling currents into the conductive substrate are suppressed, a side-contact still enables addressing the defect states in the scanning tunneling microscopy via the lateral current flow. Few-layer hBN realizes an intermediate regime in which the competition between vertical tunneling and lateral transport needs to be considered. The latter is relevant for device structures with both a thin tunneling barrier and a side-contact to the semiconducting layers.

36 MATERIALS SCIENCE↗

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.

36 MATERIALS SCIENCE↗