A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images

Chiral indices determine important properties of carbon nanotubes (CNTs). Unfortunately, their determination from high-resolution transmission electron microscopy (HRTEM) images, the most accurate method for assigning chirality, is a tedious task. We develop a Convolutional Neural Network that automatizes this process. A large and realistic training data set of CNT images is obtained by means of atomistic computer simulations coupled with the multi-slice approach for image generation. In most cases, results of the automated assignment are in excellent agreement with manual classification, and the origin of failures is identified. The current approach, which combines HRTEM imaging and deep learning algorithms allows the analysis of a statistically significant number of HRTEM images of carbon nanotubes, paving the way for robust estimates of experimental chiral distributions.Comment: for use of the discussed computer code, please contact the corresponding autho

Mapping localized surface plasmons within silver nanocubes using cathodoluminescence hyperspectral imaging

Localized surface plasmons within silver nanocubes less than 50 nm in size are investigated using high resolution cathodoluminescence hyperspectral imaging. Multivariate statistical analysis of the multidimensional luminescence dataset allows both the identification of distinct spectral features in the emission and the mapping of their spatial distribution. These results show a 490 nm peak emitted from the cube faces, with shorter wavelength luminescence coming from the vertices and edges; this provides direct experimental confirmation of theoretical predictions

Anisotropic nanomaterials: structure, growth, assembly, and functions

Comprehensive knowledge over the shape of nanomaterials is a critical factor in designing devices with desired functions. Due to this reason, systematic efforts have been made to synthesize materials of diverse shape in the nanoscale regime. Anisotropic nanomaterials are a class of materials in which their properties are direction-dependent and more than one structural parameter is needed to describe them. Their unique and fine-tuned physical and chemical properties make them ideal candidates for devising new applications. In addition, the assembly of ordered one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) arrays of anisotropic nanoparticles brings novel properties into the resulting system, which would be entirely different from the properties of individual nanoparticles. This review presents an overview of current research in the area of anisotropic nanomaterials in general and noble metal nanoparticles in particular. We begin with an introduction to the advancements in this area followed by general aspects of the growth of anisotropic nanoparticles. Then we describe several important synthetic protocols for making anisotropic nanomaterials, followed by a summary of their assemblies, and conclude with major applications

Nanoscale temperature measurement during temperature controlled in situ TEM using Al plasmon nanothermometry

International audienc