Recent studies have shown that MoS2 can undergo a structural phase transition from the 2H to 1T′ phase on Au substrates at moderate temperatures. Here, we use ultraviolet and x-ray photoelectron spectroscopy, Raman and photoluminescence spectroscopy, and scanning tunneling microscopy/spectroscopy to probe the impact of annealing exfoliated, monolayer MoS2 on Au. Our results across multiple length scales indicate that 2H-MoS2 becomes hybridized with Au upon thermal annealing without inducing the 1T′ structural phase and the bandgap can be modulated to zero width depending on the degree of hybridization. These results can be used to control the resistance of metal-MoS2 contacts at the atomic scale without introduction of defects or structural phase transitions and are important for devices such as memristors.

Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS2 sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS2, Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

Photodetectors operating in the ultraviolet (UV) play a pivotal role in applications such as ozone monitoring and biosensing. One key factor to successfully implementing such photodetectors is that they must be solar-blind to avoid detecting ambient visible and infrared light. Unfortunately, UV photodetectors based on silicon and other typical semiconductors are not natively solar-blind, since their band gap energies are in the visible range. Hexagonal boron nitride (h-BN) is an example of a wide band gap semiconductor which shows promise for use as the absorbing medium in a UV photodetector device, since its band gap is wide enough to make it inherently insensitive to light in the visible range and above. Here we report on the fabrication and characterization of a graphene-h-BN-heterostructure photodetector which utilizes a vertical geometry, in principle allowing for highly scalable production. We find that our device shows a finite photoresponse to illumination by a 254 nm light source, but not to a 365 nm source, thus suggesting that our device is solar-blind.

Scanning tunneling microscopy and spectroscopy (STM/STS) are used to electronically switch atomically-thin memristors, referred to as “atomristors”, based on a graphene/molybdenum disulfide (MoS2)/Au heterostructure. A gold-assisted exfoliation method was used to produce near-millimeter (mm) scale MoS2 on Au thin-film substrates, followed by transfer of a separately exfoliated graphene top layer. Our results reveal that it is possible to switch the conductivity of a graphene/MoS2/Au memristor stack using an STM tip. These results provide a path to further studies of atomically-thin memristors fabricated from heterostructures of two-dimensional materials such as graphene and transition metal dichalcogenides (TMDs).

No abstract