Accurate Atomic-Scale Imaging of Two-Dimensional Lattices Using Atomic Force Microscopy in Ambient Conditions

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Atomic Defect Quantification by Lateral Force Microscopy

Atomic defects in two-dimensional (2D) materials impact electronic and optoelectronic properties, such as doping and single photon emission. An understanding of defect-property relationships is essential for optimizing material performance. However, progress in understanding these critical relationships is hindered by a lack of straightforward approaches for accurate, precise, and reliable defect quantification on the nanoscale, especially for insulating materials. Here, we demonstrate that lateral force microscopy (LFM), a mechanical technique, can observe atomic defects in semiconducting and insulating 2D materials under ambient conditions. We first improve the sensitivity of LFM through consideration of cantilever mechanics. With the improved sensitivity, we use LFM to locate atomic-scale point defects on the surface of bulk MoSe2. By directly comparing LFM and conductive atomic force microscopy (CAFM) measurements on bulk MoSe2, we demonstrate that point defects observed with LFM are atomic defects in the crystal. As a mechanical technique, LFM does not require a conductive pathway, which allows defect characterization on insulating materials, such as hexagonal boron nitride (hBN). We demonstrate the ability to observe intrinsic defects in hBN and defects introduced by annealing. Our demonstration of LFM as a mechanical defect characterization technique applicable to both conductive and insulating 2D materials will enable routine defect-property determination and accelerate materials research.

Reconfiguring nucleation for CVD growth of twisted bilayer MoS2 with a wide range of twist angles

Twisted bilayer (TB) transition metal dichalcogenides (TMDCs) beyond TB-graphene are considered an ideal platform for investigating condensed matter physics, due to the moiré superlattices-related peculiar band structures and distinct electronic properties. The growth of large-area and high-quality TB-TMDCs with wide twist angles would be significant for exploring twist angle-dependent physics and applications, but remains challenging to implement. Here, we propose a reconfiguring nucleation chemical vapor deposition (CVD) strategy for directly synthesizing TB-MoS2 with twist angles from 0° to 120°. The twist angles-dependent Moiré periodicity can be clearly observed, and the interlayer coupling shows a strong relationship to the twist angles. Moreover, the yield of TB-MoS2 in bilayer MoS2 and density of TB-MoS2 are significantly improved to 17.2% and 28.9 pieces/mm2 by tailoring gas flow rate and molar ratio of NaCl to MoO3. The proposed reconfiguring nucleation approach opens an avenue for the precise growth of TB-TMDCs for both fundamental research and practical applications.