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Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader. [ABSTRACT FROM AUTHOR]

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research. [ABSTRACT FROM AUTHOR]

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments. [ABSTRACT FROM AUTHOR]

A synthetic challenge in faceted metal oxide nanocrystals (NCs) is realizing tunable localized surface plasmon resonance (LSPR) near-field response in the infrared (IR). Cube-shaped nanoparticles of noble metals exhibit LSPR spectral tunability limited to visible spectral range. Here, we describe the colloidal synthesis of fluorine, tin codoped indium oxide (F,Sn:In2O3) NC cubes with tunable IR range LSPR for around 10 nm particle sizes. Free carrier concentration is tuned through controlled Sn dopant incorporation, where Sn is an aliovalent n-type dopant in the In2O3 lattice. F shapes the NC morphology into cubes by functioning as a surfactant on the {100} crystallographic facets. Cube shaped F,Sn:In2O3 NCs exhibit narrow, shape-dependent multimodal LSPR due to corner, edge, and face centered modes. Monolayer NC arrays are fabricated through a liquid-air interface assembly, further demonstrating tunable LSPR response as NC film nanocavities that can heighten near-field enhancement (NFE). The tunable F,Sn:In2O3 NC near-field is coupled with PbS quantum dots, via the Purcell effect. The detuning frequency between the nanocavity and exciton is varied, resulting in IR near-field dependent enhanced exciton lifetime decay. LSPR near-field tunability is directly visualized through IR range scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS). STEM-EELS mapping of the spatially confined near-field in the F,Sn:In2O3 NC array interparticle gap demonstrates elevated NFE tunability in the arrays. [ABSTRACT FROM AUTHOR]

Alternate stacking of a highly conducting metallic layer with a magnetic triangular layer found in delafossite PdCrO2 provides an excellent platform for discovering intriguing correlated quantum phenomena. Thin film growth of delafossites may enable not only the tuning of the basic physical properties beyond what bulk materials can exhibit, but also the development of novel hybrid materials by interfacing with dissimilar materials, yet this has proven to be extremely challenging. Here, we report the epitaxial growth of metallic delafossite PdCrO2 films by pulsed laser epitaxy (PLE). The fundamental role of the PLE growth conditions, epitaxial strain, and chemical and structural characteristics of the substrate is investigated by growing under various growth conditions and on various types of substrates. While strain plays a large role in improving the crystallinity, the direct growth of epitaxial PdCrO2 films without impurity phases was not successful. We attribute this difficulty to both the chemical and structural dissimilarities with the substrate and volatile nature of the PdO sublayer, which make nucleation of the right phase difficult. This difficulty was overcome by growing CuCrO2 buffer layers before PdCrO2 films were grown. Unlike PdCrO2, CuCrO2 films were readily grown with a relatively wide growth window. Only a monolayer thick buffer layer was sufficient to grow the correct PdCrO2 phase. This result indicates that the epitaxy of Pd-based delafossites is extremely sensitive to the chemistry and structure of the interface, necessitating near perfect substrate materials. The resulting films are commensurately strained and show an antiferromagnetic transition at 40 K that persists down to as thin as 3.6 nm in thickness. This work provides key insights into advancing the epitaxial growth of the broader class of metallic delafossites for both studying the basic physical properties and developing new spintronic and computing devices. [ABSTRACT FROM AUTHOR]

The authors investigate the mechanism for etching of exfoliated graphene multilayers on SiO2 by low-energy (50 eV) electron irradiation using He plasma systems for electron sources. A mechanism for this etching has been previously proposed in which the incident electrons traverse the graphene and dissociate oxygen from the SiO2 substrate at the graphene/SiO2 interface. The dissociated oxygen reacts with carbon defects formed by the electron irradiation and thereby etches the graphene from below. They study etching using graphene flakes of various thicknesses on SiO2, low and higher resistivity Si, indium tin oxide (ITO), and silicon carbide (SiC). They find that thicker layer graphene on SiO2 does not etch less than thinner layers, contrary to the previously proposed model. They find that etching does not occur on low-resistivity Si and ITO. Etching occurs on higher resistivity Si and SiC, although much less than on SiO2. This is attributed to He ion sputtering and vacancy formation. From these observations, they propose that oxygen etches graphene from above rather than below. In addition, they propose He ions instead of incident electrons cause the defects that oxygen reacts with and etches. [ABSTRACT FROM AUTHOR]