The rapidly changing face of electron microscopy

Thomas, John Meurig; Leary, Rowan K.; Eggeman, Alexander S.; Midgley, Paul A.

doi:10.1016/j.cplett.2015.04.048

Cited by 27 publications

(17 citation statements)

References 57 publications

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“…Given that energydispersive detectors for X-ray analysis are now a regular feature of modern electron microscopes, as well as electron spectrometers, it is readily possible to record several distinct signals, so that it is legitimate to talk of multi-dimensional EM, as described more fully elsewhere [4,5]. In other words, EDS (energy-dispersive X-ray signals) and EELS signals, as well as images and diffraction patterns can all be simultaneously recorded.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, as a result of improved electron spectrometers, it is now possible to record the electron energy-loss spectrum (EELS) at high-resolution of individual atoms and ions in a given structure and, thereby, determine their valence state and detailed atomic environment, as well as measure high-resolution vibrational spectra of the structure to which the atom or ion is bound [2,3]. Furthermore, electron tomography (ET), especially when using AC-STEM microscopes of the type schematized in figure 2, has now become a quite popular and powerful addition to the armoury of the electron microscopists [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Reflections on the value of electron microscopy in the study of heterogeneous catalysts

Thomas

2017

Proc. R. Soc. A.

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Electron microscopy (EM) is arguably the single most powerful method of characterizing heterogeneous catalysts. Irrespective of whether they are bulk and multiphasic, or monophasic and monocrystalline, or nanocluster and even single-atom and on a support, their structures in atomic detail can be visualized in two or three dimensions, thanks to high-resolution instruments, with sub-Ångstrom spatial resolutions. Their topography, tomography, phase-purity, composition, as well as the bonding, and valence-states of their constituent atoms and ions and, in favourable circumstances, the short-range and long-range atomic order and dynamics of the catalytically active sites, can all be retrieved by the panoply of variants of modern EM. The latter embrace electron crystallography, rotation and precession electron diffraction, X-ray emission and high-resolution electron energy-loss spectra (EELS). Aberration-corrected (AC) transmission (TEM) and scanning transmission electron microscopy (STEM) have led to a revolution in structure determination. Environmental EM is already playing an increasing role in catalyst characterization, and new advances, involving special cells for the study of solid catalysts in contact with liquid reactants, have recently been deployed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reflections on the value of electron microscopy in the study of heterogeneous catalysts

Thomas

2017

Proc. R. Soc. A.

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“…Many model parameters are learned iteratively from these patches, producing a large and higher dimensional data space in which information about features and image information is captured. New ideas and techniques to exploit this large feature space are urgently required to fully realize the potential benefits from real-space under sampling.On the other end of the spectrum, extremely high brightness electron sources coupled to the obvious benefits of aberration correctors are producing extraordinarily large datasets with correlated temporal or chemical information [2]. Energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDS) are now capable of routinely producing ultra-high resolution information, and efforts to produce three dimensional tomograms with a corresponding spectra at each voxel are presently being reported.…”

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confidence: 99%

“…On the other end of the spectrum, extremely high brightness electron sources coupled to the obvious benefits of aberration correctors are producing extraordinarily large datasets with correlated temporal or chemical information [2]. Energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDS) are now capable of routinely producing ultra-high resolution information, and efforts to produce three dimensional tomograms with a corresponding spectra at each voxel are presently being reported.…”

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confidence: 99%

Self–Organizing Neural Networks: Parallels Between “Big Imaging” and Sparse Imaging in Electron Microscopy

Hujsak

Dravid

2016

Microsc Microanal

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We are witnessing interesting parallels in the microscopy community between extremely large datasets and sparse, under-sampled versions of traditional images. Ironically, methods to reduce the negative impact of electron/radiation dose by sampling fewer pixels tend to produce excessively large datasets per image [1]. Patch-based image reconstruction methods, such as hierarchical Bayesian models and non-local means methods, achieve state-of-the-art results by producing an over complete feature space through expanding the image into a large number of overlapping patches. Many model parameters are learned iteratively from these patches, producing a large and higher dimensional data space in which information about features and image information is captured. New ideas and techniques to exploit this large feature space are urgently required to fully realize the potential benefits from real-space under sampling.On the other end of the spectrum, extremely high brightness electron sources coupled to the obvious benefits of aberration correctors are producing extraordinarily large datasets with correlated temporal or chemical information [2]. Energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDS) are now capable of routinely producing ultra-high resolution information, and efforts to produce three dimensional tomograms with a corresponding spectra at each voxel are presently being reported. Automatic segmentation of regions of biological or materials samples is becoming increasingly important as the size and speed of generation of these datasets are quickly exceeding the capability of microscopists to handle them off the microscope.Perhaps unexpectedly, we thus find that sparse imaging methods have considerable overlap with imaging projects involving extensive and prolonged imaging. Both share, as discussed above, problems with the high dimensionality and large size of data with unclear informational content. Herein, we report preliminary work to address concerns of data management and image/volume analysis with selforganizing neural networks and Bayesian non-parametrics.In contrast to supervised neural networks, self-organizing and unsupervised neural networks attempt to discover underlying structure in high-dimensional data by mapping this multi-dimensional data to a series of networked nodes [3]. Thus, they may be thought of as a similarity preserving form of dimensionality reduction or clustering. Notably, such methods can perform meaningful dimensionality reduction and clustering without feature indexing or labeling, and have been shown to preserve the topology of the underlying high dimensional space [3].These maps can be combined with nonparametric clustering methods, in which the number of clusters to group similar data, is learned. Thus, when combined together these tools become a powerful force to explore and extract meaningful information from complicated high dimensional microscopy data sets. As a demonstration, the usefulness of these methods is explored in producing segmentations fro...

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“…Ultrafast electron diffraction (UED) and microscopy provide sub-angstrom spatial and femtosecond temporal resolutions that allow the direct imaging of the atomic motions [1][2][3][4]. For example, structural evolutions during phase transitions have been mapped using ultrafast electron microscopy and crystallography [5][6][7].…”

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confidence: 99%