Technological opportunities are explored to enhance detection schemes in transmission electron microscopy (TEM) that build on the detection of single-electron scattering events across the typical spectrum of interdisciplinary applications. They range from imaging with high spatiotemporal resolution to diffraction experiments at the window to quantum mechanics, where the wave-particle dualism of single electrons is evident. At the ultimate detection limit, where isolated electrons are delivered to interact with solids, we find that the beam current dominates damage processes instead of the deposited electron charge, which can be exploited to modify electron beam-induced sample alterations. The results are explained by assuming that all electron scattering are inelastic and include phonon excitation that can hardly be distinguished from elastic electron scattering. Consequently, a coherence length and a related coherence time exist that reflect the interaction of the electron with the sample and change linearly with energy loss. Phonon excitations are of small energy (<100 meV), but they occur frequently and scale with beam current in the irradiated area, which is why we can detect their contribution to beam-induced sample alterations and damage.

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

When comparing large numbers of TEM micrographs of insoluble additives in polymer-based nanocomposite systems, the ability to determine or estimate the dispersion quality (i.e. uniformity of size and/or spatial distribution) is often difficult. The objective of this study was to develop a method to quantify dispersions observed in TEM micrographs that enables both a numerical “ranking” to be assigned to individual dispersions as well as tabulation a multitude of images acquired over time. Several methods were reviewed and applied to a set of TEM dispersion images of an insoluble additive in polystyrene. Projected area diameter, particle area, and Euclidean distance between particle centroids were chosen from all the particle size distribution and spatial distribution parameters present in the literature, but none successfully yielded a quantitative indicator of dispersion quality for the micrographs. In contrast, generating cumulative volume percent curves for each sample appeared to be a preferred method of quantifying and comparing dispersions in TEM micrographs. The volume diameter values obtained by this method can be used for “ranking” and tabulation of dispersion quality and account for both “good” additive dispersions (i.e. those with small domains of a narrow size range around 1 μm or less) and “bad” additive dispersions (i.e. those with non-uniform domains ranging in size by several microns or more). As a result, the numerical values generated by this method can be used to quantitatively determine correlations between the dispersion quality of nanoparticles in polymer-based nanocomposite materials and various macroscale physical and/or performance properties of such materials. This method’s precision was statistically determined to decrease with increasing particle size and be heavily dependent on representative sampling.

We describe the growth of amorphous carbon nanofibers (CNFs) from iron-encapsulated dendrimer catalysts at ambient temperature and pressure conditions. Both fourth-generation poly (propyleneimine) (PPI) and poly(amidoamine) (PAMAM) dendrimers were suitable macromolecular hosts for the catalytic species. Average nanofiber diameters range from 10 - 15 nm, with lengths in excess of 20 microns.