Multilayer Lateral Heterostructures of Van Der Waals Crystals with Sharp, Carrier–Transparent Interfaces

Research on engineered materials that integrate different 2D crystals has largely focused on two prototypical heterostructures: Vertical van der Waals stacks and lateral heterostructures of covalently stitched monolayers. Extending lateral integration to few layer or even multilayer van der Waals crystals could enable architectures that combine the superior light absorption and photonic properties of thicker crystals with close proximity to interfaces and efficient carrier separation within the layers, potentially benefiting applications such as photovoltaics. Here, the realization of multilayer heterstructures of the van der Waals semiconductors SnS and GeS with lateral interfaces spanning up to several hundred individual layers is demonstrated. Structural and chemical imaging identifies {110} interfaces that are perpendicular to the (001) layer plane and are laterally localized and sharp on a 10 nm scale across the entire thickness. Cathodoluminescence spectroscopy provides evidence for a facile transfer of electron-hole pairs across the lateral interfaces, indicating covalent stitching with high electronic quality and a low density of recombination centers

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extract information from atomically resolved images including location of the atomic species and type of defects. We develop a 'weakly-supervised' approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular 'rotor'. This deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data

Direct Fabrication of Atomically Defined Pores in MXenes

Controlled fabrication of nanopores in atomically thin two-dimensional material offers the means to create robust membranes needed for ion transport, nanofiltration, and DNA sensing. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation using electrons or ions; however, aberration-corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a highly energetic, sub-angstrom sized electron beam for atomic manipulation along with atomic resolution imaging. Here, we utilize a method for automated nanopore fabrication with real-time atomic visualization to enhance our mechanistic understanding of beam-induced transformations. Additionally, an electron beam simulation technique, Electron-Beam Simulator (E-BeamSim) was developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the 2D MXene Ti3C2Tx, we explore the influence of temperature on nanopore fabrication by tracking atomic transformation pathways and find that at room temperature, electron beam irradiation induces random displacement of atoms and results in a pileup of titanium atoms at the nanopore edge. This pileup was confirmed and demonstrated in E-BeamSim simulations around the small, milled area in the MXene monolayer. At elevated temperatures, the surface functional groups on MXene are effectively removed, and the mobility of atoms increases, which results in atomic transformations that lead to the selective removal of atoms layer by layer. Through controllable manufacture using e-beam milling fabrication, the production and then characterization of the fabricated defects can be better understood for future work. This work can lead to the development of defect engineering techniques within functionalized MXene layers.Comment: Experimental and simulations on the electron beam interactions with MXene monolayers to form nanopores as a function of temperatur

Designing Atomic Edge Structures in 2D Transition Metal Dichalcogenides for Improved Catalytic Activity

Designing new materials for functional applications depends upon our ability to understand and correlate the materials structure and chemistry to functional material properties. This is even more important for two-dimensional (2D) materials where thicknesses are on the order of a single atom to a few-atomic layers; therefore, any structural or chemical modification at these length scales can have a profound effect on modifying physical and chemical properties. 2D transition metal dichalcogenides (TMDs) such as MoS2 have emerged as a promising catalyst for the hydrogen evolution reaction with defects such as vacancies and edges being linked to high catalytic active sites as opposed to basal planes for the hydrogen evolution reaction (HER) [1]. Based on these findings it is important to develop controlled synthesis methods that will promote the formation of atomic edge structures engineered for high catalytic HER activity