There is increased interest in the use of cellulose nanomaterials for the mechanical reinforcement of composites due to their high stiffness and strength. However, challenges remain in accurately determining their distribution within composite microstructures. We report the use of a range of techniques used to image aggregates of cellulose nanocrystals (CNCs) greater than 10 µm2 within a model thermoplastic polymer. While Raman imaging accurately determines CNC aggregate size, it requires extended periods of analysis and the limited observable area results in poor reproducibility. In contrast, staining the CNCs with a fluorophore enables rapid acquisition with high reproducibility, but overestimates the aggregate size as CNC content increases. Multi-channel spectral confocal laser scanning microscopy is presented as an alternative technique that combines the accuracy of Raman imaging with the speed and reproducibility of conventional confocal laser scanning microscopy, enabling the rapid determination of CNC aggregate distribution within composites.

iSpectra is an open source and system-independent toolbox for the analysis of spectral images (SIs) recorded on energy-dispersive spectroscopy (EDS) systems attached to scanning electron microscopes (SEMs). The aim of iSpectra is to assign pixels with similar spectral content to phases, accompanied by cumulative phase spectra with superior counting statistics for quantification. Pixel-to-phase assignment starts with a threshold-based pre-sorting of spectra to create groups of pixels with identical elemental budgets, similar to a method described by van Hoek (2014). Subsequent merging of groups and re-assignments of pixels using elemental or principle component histogram plots enables the user to generate chemically and texturally plausible phase maps. A variety of standard image processing algorithms can be applied to groups of pixels to optimize pixel-to-phase assignments, such as morphology operations to account for overlapping excitation volumes over pixels located at phase boundaries. iSpectra supports batch processing and allows pixel-to-phase assignments to be applied to an unlimited amount of SIs, thus enabling phase mapping of large area samples like petrographic thin sections.

As part of an ongoing effort to increase image reproducibility and fidelity in addition to improving cross-instrument consistency, we have proposed using four separate instrument quality tests to augment the ones we have previously reported. These four tests assessed the following areas: (1) objective lens quality, (2) resolution, (3) accuracy of the wavelength information from spectral detectors, and (4) the accuracy and quality of spectral separation algorithms. Data were received from 55 laboratories located in 18 countries. The largest source of errors across all tests was user error which could be subdivided between failure to follow provided protocols and improper use of the microscope. This truly emphasizes the importance of proper rigorous training and diligence in performing confocal microscopy experiments and equipment evaluations. It should be noted that there was no discernible difference in quality between confocal microscope manufactures. These tests, as well as others previously reported, will help assess the quality of confocal microscopy equipment and will provide a means to track equipment performance over time. From 62 to 97% of the data sets sent in passed the various tests demonstrating the usefulness and appropriateness of these tests as part of a larger performance testing regiment.

A hybrid nanobeam diffraction/imaging method, which combines well-developed diffraction imaging with nanobeam diffraction (NBD) pattern analysis, is described for structural mapping of disordered materials. Spatially resolved crystallographic information is obtained by NBD imaging by collecting NBD patterns at predefined intervals within a field of interest. The resulting dataset of NBD patterns is preprocessed to produce a spectral-imaging-like dataset and is further analyzed via multivariate statistical analysis methods in order to extract the relevant structural components and their distribution within the area of the sample under study without prior knowledge. Additional radial distribution function analysis of either the principal components or averaged data provides real-space maps of short-range order within the field of interest. This technique is demonstrated for two systems, one with multiple amorphous phases and one with multiple phases (amorphous and nanocrystalline) with similar chemistry.

A new aberration-corrected scanning transmission electron microscope equipped with an array of Si-drift energy-dispersive X-ray spectrometers has been utilized to acquire spectral image data at atomic resolution. The resulting noisy data were subjected to multivariate statistical analysis to noise filter, remove an unwanted and partially overlapping non-sample-specific X-ray signal, and extract the relevant correlated X-ray signals (e.g., channels with L and K lines). As an example, the Y2Ti2O7 pyrochlore-structured oxide (assumed here to be ideal) was interrogated at the [011] projection. In addition to pure columns of Y and Ti, at this projection, there are also mixed 50-50 at. % Y-Ti columns. An attempt at atomic-resolution quantification is presented. The method proposed here is to subtract the non-column-specific signal from the elemental components and then quantify the data based upon an internally derived k-factor. However, a theoretical basis to predict this non-column-specific signal is needed to make this generally applicable.

Cellular autofluorescence, though ubiquitous when imaging cells and tissues, is often assumed to be small in comparison to the signal of interest. Uniform estimates of autofluorescence intensity obtained from separate control specimens are commonly employed to correct for autofluorescence. While these may be sufficient for high signal-to-background applications, improvements in detector and probe technologies and introduction of spectral imaging microscopes have increased the sensitivity of fluorescence imaging methods, exposing the possibility of effectively probing the low signal-to-background regime. With spectral imaging, reliable monitoring of signals near or even below the noise levels of the microscope is possible if compensation for autofluorescence and background signals can be performed accurately. We demonstrate the importance of accurate autofluorescence modeling and the utility of spectral imaging and multivariate analysis methods using a case study focusing on fluorescence confocal spectral imaging of host-pathogen interactions. In this application fluorescent proteins are produced when Francisella novicida invade host macrophage cells. The resulting analyte signal is spectrally overlapped and typically weaker than the cellular autofluorescence. In addition to discussing the advantages of spectral imaging for following pathogen invasion, we present the spectral properties and cellular origin of macrophage autofluorescence.

Multivariate statistical analysis methods have been applied to scanning transmission electron microscopy (STEM) energy-dispersive X-ray spectral images. The particular application of the multivariate curve resolution (MCR) technique provides a high spectral contrast view of the raw spectral image. The power of this approach is demonstrated with a microelectronics failure analysis. Specifically, an unexpected component describing a chemical contaminant was found, as well as a component consistent with a foil thickness change associated with the focused ion beam specimen preparation process. The MCR solution is compared with a conventional analysis of the same spectral image data set.

A comprehensive three-dimensional (3D) microanalysis procedure using a combined scanning electron microscope (SEM)/focused ion beam (FIB) system equipped with an energy-dispersive X-ray spectrometer (EDS) has been developed. The FIB system was used first to prepare a site-specific region for X-ray microanalysis followed by the acquisition of an electron-beam generated X-ray spectral image. A small section of material was then removed by the FIB, followed by the acquisition of another X-ray spectral image. This serial sectioning procedure was repeated 10–12 times to sample a volume of material. The series of two-spatial-dimension spectral images were then concatenated into a single data set consisting of a series of volume elements or voxels each with an entire X-ray spectrum. This four-dimensional (three real space and one spectral dimension) spectral image was then comprehensively analyzed with Sandia's automated X-ray spectral image analysis software. This technique was applied to a simple Cu-Ag eutectic and a more complicated localized corrosion study where the powerful site-specific comprehensive analysis capability of tomographic spectral imaging (TSI) combined with multivariate statistical analysis is demonstrated.