Spectroscopic Observation and Modeling of Photonic Modes in CeO2 Nanocubes

Photonic modes in dielectric nanostructures, e.g., wide gap semiconductor like CeO2 (ceria), has potential for various applications such as light harvesting and information transmission. To fully understand the properties of such phenomenon in nanoscale, we applied electron energy-loss spectroscopy (EELS) in scanning transmission electron microscope (STEM) to detect such modes in a well-defined ceria nanocube. Through spectra and mapping, we demonstrated a geometrical difference of mode excitation. By comparing various spectra taken at different location relative to the cube, we also showed the transmission properties of the mode. To confirm our observation, we performed EELS simulation with finite-element dielectric calculations in COMSOL Multiphysics. We also revealed the origin of the modes through the calculation. We purposed a simple analytical model to estimate the energy of photonic modes as well. In all, this work gave a fine description of the photonic modes' properties in nanostructures, while demonstrating the advantage of EELS in characterizing optical phenomena in nanoscale

Direct quantitative measurement of compositional enrichment and variations in InyGa1−yAs quantum dots

Assessment of the composition of quantum dots on the nanoscale is crucial for a deeper understanding of both the growth mechanisms and the properties of these materials. In this letter, we discuss a direct method to obtain a quantitative evaluation of the In variation across nanometer-sized InGaAs quantum dots embedded in a GaAs matrix, by means of electron energy-loss spectroscopy in a scanning transmission electron microscope

Atomic Level Strain Induced by Static and Dynamic Oxygen Vacancies on Reducible Oxide Surfaces

Surface strain often controls properties of the material including charge transport and chemical reactivity. Localized surface strain is measured with atomic resolution on (111) ceria nanoparticle surfaces using environmental transmission electron microscopy under different redox conditions. Density Functional Theory (DFT) coupled with TEM image simulations have been used for aid in interpreting the experimental data. Oxygen vacancy creation/annihilation introduces strain at surface and near surface regions on cation sublattice. Static and fluxional strainmaps are generated from images at these different conditions and compared. While fluxional strain is highest at locations associated with unstable vacancies at active sites, highly inhomogeneous static strain fields comprising of alternating tensile/compressing strain is seen at surface and subsurfaces linked to the presence of stable oxygen vacancies. Interestingly, both stable and unstable oxygen vacancies are found within a few atomic spacing of each other on the same surface. The static strain pattern depends on the ambient inside TEM. Oxidizing environments tend to lower vacancy concentrations at the surface whereas a highly reducing environment created using high electron dose creates oxygen vacancies everywhere (bulk and surfaces) in the nanoparticle

Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner. FIRENet learns image specific feature representations by way of 3D fabric network architecture that contains exponential number of sub-architectures to handle various protocols and coverage of anatomical regions and structures. The fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to extract local and image-level features at a fine selection of scales. The fabric is constructed with weighted edges allowing the learnt features to dynamically adapt to the training data at an architecture level. Conditional padding modules, which are integrated into the network to reinsert voxels discarded by feature pooling, allow the network to inherently process different-size images at their original resolutions. FIRENet was trained for feature learning via automated semantic segmentation of pelvic structures and obtained a state-of-the-art median DSC score of 0.867. FIRENet was also simultaneously trained on MR (Magnatic Resonance) images acquired from 3D examinations of musculoskeletal elements in the (hip, knee, shoulder) joints and a public OAI knee dataset to perform automated segmentation of bone across anatomy. Transfer learning was used to show that the features learnt through the pelvic segmentation helped achieve improved mean DSC scores of 0.962, 0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure