Visualizing the Effect of an Electrostatic Gate with Angle-Resolved Photoemission Spectroscopy

Electrostatic gating is pervasive in materials science, yet its effects on the electronic band structure of materials has never been revealed directly by angle-resolved photoemission spectroscopy (ARPES), the technique of choice to non-invasively probe the electronic band structure of a material. By means of a state-of-the-art ARPES setup with sub-micron spatial resolution, we have investigated a heterostructure composed of Bernal-stacked bilayer graphene (BLG) on hexagonal boron nitride and deposited on a graphite flake. By voltage biasing the latter, the electric field effect is directly visualized on the valence band as well as on the carbon 1s core level of BLG. The band gap opening of BLG submitted to a transverse electric field is discussed and the importance of intralayer screening is put forward. Our results pave the way for new studies that will use momentum-resolved electronic structure information to gain insight on the physics of materials submitted to the electric field effect

Giant Orbital Magnetic Moments and Paramagnetic Shift in Artificial Relativistic Atoms and Molecules

Materials such as graphene and topological insulators host massless Dirac fermions that enable the study of relativistic quantum phenomena. Single quantum dots and coupled quantum dots formed with massless Dirac fermions can be viewed as artificial relativistic atoms and molecules, respectively. Such structures offer a unique testbed to study atomic and molecular physics in the ultrarelativistic regime (particle speed close to the speed of light). Here we use a scanning tunnelling microscope to create and probe single and coupled electrostatically defined graphene quantum dots to unravel the magnetic-field responses of artificial relativistic nanostructures. We observe a giant orbital Zeeman splitting and orbital magnetic moment up to ~70 meV T-1 and ~600μB (μB, Bohr magneton) in single graphene quantum dots. For coupled graphene quantum dots, Aharonov-Bohm oscillations and a strong Van Vleck paramagnetic shift of ~20 meV T-2 are observed. Our findings provide fundamental insights into relativistic quantum dot states, which can be potentially leveraged for use in quantum information science

Control of Giant Topological Magnetic Moment and Valley Splitting in Trilayer Graphene

Bloch states of electrons in honeycomb two-dimensional crystals with multi-valley band structure and broken inversion symmetry have orbital magnetic moments of a topological nature. In crystals with two degenerate valleys, a perpendicular magnetic field lifts the valley degeneracy via a Zeeman effect due to these magnetic moments, leading to magnetoelectric effects which can be leveraged for creating valleytronic devices. In this work, we demonstrate that trilayer graphene with Bernal stacking, (ABA TLG) hosts topological magnetic moments with a large and widely tunable valley g-factor, reaching a value 1050 at the extreme of the studied parametric range. The reported experiment consists in sublattice-resolved scanning tunneling spectroscopy under perpendicular electric and magnetic fields that control the TLG bands. The tunneling spectra agree very well with the results of theoretical modeling that includes the full details of the TLG tight-binding model and accounts for a quantum-dot-like potential profile formed electrostatically under the scanning tunneling microscope tip.Comment: Manuscript and Supporting Information update

Observation of Giant Orbital Magnetic Moments and Paramagnetic Shift in Artificial Relativistic Atoms and Molecules

Massless Dirac fermions have been observed in various materials such as graphene and topological insulators in recent years, thus offering a solid-state platform to study relativistic quantum phenomena. Single quantum dots (QDs) and coupled QDs formed with massless Dirac fermions can be viewed as artificial relativistic atoms and molecules, respectively. Such structures offer a unique platform to study atomic and molecular physics in the ultra-relativistic regime. Here, we use a scanning tunneling microscope to create and probe single and coupled electrostatically defined graphene QDs to unravel the unique magnetic field responses of artificial relativistic nanostructures. Giant orbital Zeeman splitting and orbital magnetic moment are observed in single graphene QDs. While for coupled graphene QDs, Aharonov Bohm oscillations and strong Van Vleck paramagnetic shift are observed. Such properties of artificial relativistic atoms and molecules can be leveraged for novel magnetic field sensing modalities

Denoising Scanning Tunneling Microscopy Images of Graphene with Supervised Machine Learning

Machine learning (ML) methods are extraordinarily successful at denoising photographic images. The application of such denoising methods to scientific images is, however, often complicated by the difficulty in experimentally obtaining a suitable expected result as an input to training the ML network. Here, we propose and demonstrate a simulation-based approach to address this challenge for denoising atomic-scale scanning tunneling microscopy (STM) images, which consists of training a convolutional neural network on STM images simulated based on a tight-binding electronic structure model. As model materials, we consider graphite and its mono- and few-layer counterpart, graphene. With the goal of applying it to any experimental STM image obtained on graphitic systems, the network was trained on a set of simulated images with varying characteristics such as tip height, sample bias, atomic-scale defects, and non-linear background. Denoising of both simulated and experimental images with this approach is compared to that of commonly-used filters, revealing a superior outcome of the ML method in the removal of noise as well as scanning artifacts - including on features not simulated in the training set. An extension to larger STM images is further discussed, along with intrinsic limitations arising from training set biases that discourage application to fundamentally unknown surface features. The approach demonstrated here provides an effective way to remove noise and artifacts from typical STM images, yielding the basis for further feature discernment and automated processing.Comment: Includes S

High‑Throughput Electron Diffraction Reveals a Hidden Novel Metal–Organic Framework for Electrocatalysis

AbstractMetal‐organic frameworks (MOFs) are known for their versatile combination of inorganic building units and organic linkers, which offers immense opportunities in a wide range of applications. However, many MOFs are typically synthesized as multiphasic polycrystalline powders, which are challenging for studies by X‐ray diffraction. Therefore, developing new structural characterization techniques is highly desired in order to accelerate discoveries of new materials. Here, we report a high‐throughput approach for structural analysis of MOF nano‐ and sub‐microcrystals by three‐dimensional electron diffraction (3DED). A new zeolitic‐imidazolate framework (ZIF), denoted ZIF‐EC1, was first discovered in a trace amount during the study of a known ZIF‐CO3‐1 material by 3DED. The structures of both ZIFs were solved and refined using 3DED data. ZIF‐EC1 has a dense 3D framework structure, which is built by linking mono‐ and bi‐nuclear Zn clusters and 2‐methylimidazolates (mIm−). With a composition of Zn3(mIm)5(OH), ZIF‐EC1 exhibits high N and Zn densities. We show that the N‐doped carbon material derived from ZIF‐EC1 is a promising electrocatalyst for oxygen reduction reaction (ORR). The discovery of this new MOF and its conversion to an efficient electrocatalyst highlights the power of 3DED in developing new materials and their applications

Result of a year-long animal survey in a state-owned forest farm in Beijing, China

BackgroundArtificial forest can have great potential in serving as habitat to wildlife, depending on different management methods. As the state-owned forest farms now play a new role in ecological conservation in China, the biological richness of this kind of land-use type is understudied. Once owned by a mining company, a largest state-owned forest farm, Jingxi Forest Farm, has been reformed to be a state-owned forest farm with the purpose of conservation since 2017. Although this 116.4 km2 forest farm holds a near-healthy montaine ecosystem very representative in North China, a large proportion of artificial coniferous forest in the forest farm has been proven to hold less biodiversity than natural vegetation. This situation, however, provides a great opportunity for ecological restoration and biodiversity conservation. Therefore, from November 2019 to December 2020, we conducted a set of biodiversity surveys, whose results will serve as a baseline for further restoration and conservation.New informationHere, we report the result of a multi-taxa fauna diversity survey conducted in Jingxi Forest Farm mainly in year 2020 with explicit spatial information. It is the first survey of its kind conducted in this area, revealing a total of 19 species of mammals, 86 birds, four reptiles, two amphibians and one fish species, as well as 101 species of insects. Four species of mammals are identified as data-poor species as they have less than 100 occurrence records with coordination in the GBIF database. One species of insect, representing one new provincial record genus of Beijing, is reported