Incommensurate Antiferromagnetic Order in Weakly Frustrated Two-Dimensional van der Waals Insulator CrPSe3.

Although magnetic order is suppressed by a strong frustration, it appears in complex forms such as a cycloid or spin density wave in weakly frustrated systems. Herein, we report a weakly magnetically frustrated two-dimensional (2D) van der Waals material CrPSe3. Polycrystalline CrPSe3 was synthesized at an optimized temperature of 700 °C to avoid the formation of any secondary phases (e.g., Cr2Se3). The antiferromagnetic transition appeared at TN ≈ 127 K with a large Curie-Weiss temperature θCW ≈ -301 K via magnetic susceptibility measurements, indicating weak frustration in CrPSe3 with a frustration factor of f (|θCW|/TN) ≈ 2.4. Evidently, the formation of a long-range incommensurate antiferromagnetic order was revealed by neutron diffraction measurements at low temperatures (below 120 K). The monoclinic crystal structure of the C2/m symmetry is preserved over the studied temperature range down to 20 K, as confirmed by Raman spectroscopy measurements. Our findings on the incommensurate antiferromagnetic order in 2D magnetic materials, not previously observed in the MPX3 family, are expected to enrich the physics of magnetism at the 2D limit, thereby opening opportunities for their practical applications in spintronics and quantum devices.

Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision.

Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. One such material is hexagonal boron nitride (hBN), an isomorph of graphene with a very indistinguishable layered structure. In order to use these materials for research and product development, the most effective method is mechanical exfoliation where single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of hBN based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectoRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes ([Formula: see text] atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background.

Nanoscale Raman Characterization of a 2D Semiconductor Lateral Heterostructure Interface.

The nature of the interface in lateral heterostructures of 2D monolayer semiconductors including its composition, size, and heterogeneity critically impacts the functionalities it engenders on the 2D system for next-generation optoelectronics. Here, we use tip-enhanced Raman scattering (TERS) to characterize the interface in a single-layer MoS2/WS2 lateral heterostructure with a spatial resolution of 50 nm. Resonant and nonresonant TERS spectroscopies reveal that the interface is alloyed with a size that varies over an order of magnitude─from 50 to 600 nm─within a single crystallite. Nanoscale imaging of the continuous interfacial evolution of the resonant and nonresonant Raman spectra enables the deconvolution of defect activation, resonant enhancement, and material composition for several vibrational modes in single-layer MoS2, MoxW1-xS2, and WS2. The results demonstrate the capabilities of nanoscale TERS spectroscopy to elucidate macroscopic structure-property relationships in 2D materials and to characterize lateral interfaces of 2D systems on length scales that are imperative for devices.