Local atomic stacking and symmetry in twisted graphene trilayers.

Moiré superlattices formed by twisting trilayers of graphene are a useful model for studying correlated electron behaviour and offer several advantages over their formative bilayer analogues, including a more diverse collection of correlated phases and more robust superconductivity. Spontaneous structural relaxation alters the behaviour of moiré superlattices considerably and has been suggested to play an important role in the relative stability of superconductivity in trilayers. Here we use an interferometric four-dimensional scanning transmission electron microscopy approach to directly probe the local graphene layer alignment over a wide range of trilayer graphene structures. Our results inform a thorough understanding of how reconstruction modulates the local lattice symmetries crucial for establishing correlated phases in twisted graphene trilayers, evincing a relaxed structure that is markedly different from that proposed previously.

Near Room-Temperature Intrinsic Exchange Bias in an Fe Intercalated ZrSe2 Spin Glass.

Some magnetic systems display a shift in the center of their magnetic hysteresis loop away from zero field, a phenomenon termed exchange bias. Despite the extensive use of the exchange bias effect, particularly in magnetic multilayers, for the design of spin-based memory/electronics devices, a comprehensive mechanistic understanding of this effect remains a longstanding problem. Recent work has shown that disorder-induced spin frustration might play a key role in exchange bias, suggesting new materials design approaches for spin-based electronic devices that harness this effect. Here, we design a spin glass with strong spin frustration induced by magnetic disorder by exploiting the distinctive structure of Fe intercalated ZrSe2, where Fe(II) centers are shown to occupy both octahedral and tetrahedral interstitial sites and to distribute between ZrSe2 layers without long-range structural order. Notably, we observe behavior consistent with a magnetically frustrated and multidegenerate ground state in these Fe0.17ZrSe2 single crystals, which persists above room temperature. Moreover, this magnetic frustration leads to a robust and tunable exchange bias up to 250 K. These results not only offer important insights into the effects of magnetic disorder and frustration in magnetic materials generally, but also highlight as design strategy the idea that a large exchange bias can arise from an inhomogeneous microscopic environment without discernible long-range magnetic order. In addition, these results show that intercalated TMDs like Fe0.17ZrSe2 hold potential for spintronic technologies that can achieve room temperature applications.

Anomalous Interfacial Electron-Transfer Kinetics in Twisted Trilayer Graphene Caused by Layer-Specific Localization.

Interfacial electron-transfer (ET) reactions underpin the interconversion of electrical and chemical energy. It is known that the electronic state of electrodes strongly influences ET rates because of differences in the electronic density of states (DOS) across metals, semimetals, and semiconductors. Here, by controlling interlayer twists in well-defined trilayer graphene moirés, we show that ET rates are strikingly dependent on electronic localization in each atomic layer and not the overall DOS. The large degree of tunability inherent to moiré electrodes leads to local ET kinetics that range over 3 orders of magnitude across different constructions of only three atomic layers, even exceeding rates at bulk metals. Our results demonstrate that beyond the ensemble DOS, electronic localization is critical in facilitating interfacial ET, with implications for understanding the origin of high interfacial reactivity typically exhibited by defects at electrode-electrolyte interfaces.

Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis.

Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

Hard Ferromagnetism Down to the Thinnest Limit of Iron-Intercalated Tantalum Disulfide.

Two-dimensional (2D) magnetic crystals hold promise for miniaturized and ultralow power electronic devices that exploit spin manipulation. In these materials, large, controllable magnetocrystalline anisotropy (MCA) is a prerequisite for the stabilization and manipulation of long-range magnetic order. In known 2D magnetic crystals, relatively weak MCA typically results in soft ferromagnetism. Here, we demonstrate that ferromagnetic order persists down to the thinnest limit of FexTaS2 (Fe-intercalated bilayer 2H-TaS2) with giant coercivities up to 3 T. We prepare Fe-intercalated TaS2 by chemical intercalation of van der Waals-layered 2H-TaS2 crystals and perform variable-temperature transport, transmission electron microscopy, and confocal Raman spectroscopy measurements to shed new light on the coupled effects of dimensionality, degree of intercalation, and intercalant order/disorder on the hard ferromagnetic behavior of FexTaS2. More generally, we show that chemical intercalation gives access to a rich synthetic parameter space for low-dimensional magnets, in which magnetic properties can be tailored by the choice of the host material and intercalant identity/amount, in addition to the manifold distinctive degrees of freedom available in atomically thin, van der Waals crystals.

Simultaneous Successive Twinning Captured by Atomic Electron Tomography.

Shape-controlled synthesis of multiply twinned nanostructures is heavily emphasized in nanoscience, in large part due to the desire to control the size, shape, and terminating facets of metal nanoparticles for applications in catalysis. Direct control of the size and shape of solution-grown nanoparticles relies on an understanding of how synthetic parameters alter nanoparticle structures during synthesis. However, while outcome populations can be effectively studied with standard electron microscopy methods, transient structures that appear during some synthetic routes are difficult to study using conventional high resolution imaging methods due to the high complexity of the 3D nanostructures. Here, we have studied the prevalence of transient structures during growth of multiply twinned particles and employed atomic electron tomography to reveal the atomic-scale three-dimensional structure of a Pd nanoparticle undergoing a shape transition. By identifying over 20 000 atoms within the structure and classifying them according to their local crystallographic environment, we observe a multiply twinned structure consistent with a simultaneous successive twinning from a decahedral to icosahedral structure.

Machine Learning Pipeline for Segmentation and Defect Identification from High-Resolution Transmission Electron Microscopy Data.

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two-step pipeline for the analysis of high-resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for the detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from the amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open-source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape, and defect presence, enabling the detection of correlations between these features.

Synthetic WSe2 monolayers with high photoluminescence quantum yield.

In recent years, there have been tremendous advancements in the growth of monolayer transition metal dichalcogenides (TMDCs) by chemical vapor deposition (CVD). However, obtaining high photoluminescence quantum yield (PL QY), which is the key figure of merit for optoelectronics, is still challenging in the grown monolayers. Specifically, the as-grown monolayers often exhibit lower PL QY than their mechanically exfoliated counterparts. In this work, we demonstrate synthetic tungsten diselenide (WSe2) monolayers with PL QY exceeding that of exfoliated crystals by over an order of magnitude. PL QY of ~60% is obtained in monolayer films grown by CVD, which is the highest reported value to date for WSe2 prepared by any technique. The high optoelectronic quality is enabled by the combination of optimizing growth conditions via tuning the halide promoter ratio, and introducing a simple substrate decoupling method via solvent evaporation, which also mechanically relaxes the grown films. The achievement of scalable WSe2 with high PL QY could potentially enable the emergence of technologically relevant devices at the atomically thin limit.

Engineering the Photoresponse of InAs Nanowires.

We report on individual-InAs nanowire optoelectronic devices which can be tailored to exhibit either negative or positive photoconductivity (NPC or PPC). The NPC photoresponse time and magnitude is found to be highly tunable by varying the nanowire diameter under controlled growth conditions. Using hysteresis characterization, we decouple the observed photoexcitation-induced hot electron trapping from conventional electric field-induced trapping to gain a fundamental insight into the interface trap states responsible for NPC. Furthermore, we demonstrate surface passivation without chemical etching which both enhances the field-effect mobility of the nanowires by approximately an order of magnitude and effectively eliminates the hot carrier trapping found to be responsible for NPC, thus restoring an "intrinsic" positive photoresponse. This opens pathways toward engineering semiconductor nanowires for novel optical-memory and photodetector applications.