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Title: Compressive Classification for TEM-EELS

Abstract

Electron energy loss spectroscopy (EELS) is typically conducted in STEM mode with a spectrometer, or in TEM mode with energy selction. These methods produce a 3D data set (x, y, energy). Some compressive sensing [1,2] and inpainting [3,4,5] approaches have been proposed for recovering a full set of spectra from compressed measurements. In many cases the final form of the spectral data is an elemental map (an image with channels corresponding to elements). This means that most of the collected data is unused or summarized. We propose a method to directly recover the elemental map with reduced dose and acquisition time. We have designed a new computational TEM sensor for compressive classification [6,7] of energy loss spectra called TEM-EELS.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1379437
Report Number(s):
PNNL-SA-124106
Journal ID: ISSN 1431-9276; applab
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: Microscopy and Microanalysis; Journal Volume: 23; Journal Issue: S1
Country of Publication:
United States
Language:
English

Citation Formats

Hao, Weituo, Stevens, Andrew, Yang, Hao, Gehm, Michael, and Browning, Nigel D. Compressive Classification for TEM-EELS. United States: N. p., 2017. Web. doi:10.1017/S1431927617001222.
Hao, Weituo, Stevens, Andrew, Yang, Hao, Gehm, Michael, & Browning, Nigel D. Compressive Classification for TEM-EELS. United States. doi:10.1017/S1431927617001222.
Hao, Weituo, Stevens, Andrew, Yang, Hao, Gehm, Michael, and Browning, Nigel D. Sat . "Compressive Classification for TEM-EELS". United States. doi:10.1017/S1431927617001222.
@article{osti_1379437,
title = {Compressive Classification for TEM-EELS},
author = {Hao, Weituo and Stevens, Andrew and Yang, Hao and Gehm, Michael and Browning, Nigel D.},
abstractNote = {Electron energy loss spectroscopy (EELS) is typically conducted in STEM mode with a spectrometer, or in TEM mode with energy selction. These methods produce a 3D data set (x, y, energy). Some compressive sensing [1,2] and inpainting [3,4,5] approaches have been proposed for recovering a full set of spectra from compressed measurements. In many cases the final form of the spectral data is an elemental map (an image with channels corresponding to elements). This means that most of the collected data is unused or summarized. We propose a method to directly recover the elemental map with reduced dose and acquisition time. We have designed a new computational TEM sensor for compressive classification [6,7] of energy loss spectra called TEM-EELS.},
doi = {10.1017/S1431927617001222},
journal = {Microscopy and Microanalysis},
number = S1,
volume = 23,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}
}
  • One of the main limitations of imaging at high spatial and temporal resolution during in-situ TEM experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing methods [1, 2, 3, 4] to increase the framerate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integrated into amore » single camera frame during the acquisition process, and then extracted upon readout using statistical compressive sensing inversion. Our simulations show that it should be possible to increase the speed of any camera by at least an order of magnitude. Compressive Sensing (CS) combines sensing and compression in one operation, and thus provides an approach that could further improve the temporal resolution while correspondingly reducing the electron dose rate. Because the signal is measured in a compressive manner, fewer total measurements are required. When applied to TEM video capture, compressive imaging couled improve acquisition speed and reduce the electron dose rate. CS is a recent concept, and has come to the forefront due the seminal work of Candès [5]. Since the publication of Candès, there has been enormous growth in the application of CS and development of CS variants. For electron microscopy applications, the concept of CS has also been recently applied to electron tomography [6], and reduction of electron dose in scanning transmission electron microscopy (STEM) imaging [7]. To demonstrate the applicability of coded aperture CS video reconstruction for atomic level imaging, we simulate compressive sensing on observations of Pd nanoparticles and Ag nanoparticles during exposure to high temperatures and other environmental conditions. Figure 1 highlights the results from the Pd nanoparticle experiment. On the left, 10 frames are reconstructed from a single coded frame—the original frames are shown for comparison. On the right a selection of three frames are shown from reconstructions at compression levels 10,20,30. The reconstructions, which are not post-processed, are true to the original and degrade in a straightforward manner. The final choice of compression level will obviously depend on both the temporal and spatial resolution required for a specific imaging task, but the results indicate that an increase in speed of better than an order of magnitude should be possible for all experiments. References: [1] P Llull, X Liao, X Yuan et al. Optics express 21(9), (2013), p. 10526. [2] J Yang, X Yuan, X Liao et al. Image Processing, IEEE Trans 23(11), (2014), p. 4863. [3] X Yuan, J Yang, P Llull et al. In ICIP 2013 (IEEE), p. 14. [4] X Yuan, P Llull, X Liao et al. In CVPR 2014. p. 3318. [5] EJ Candès, J Romberg and T Tao. Information Theory, IEEE Trans 52(2), (2006), p. 489. [6] P Binev, W Dahmen, R DeVore et al. In Modeling Nanoscale Imaging in Electron Microscopy, eds. T Vogt, W Dahmen and P Binev (Springer US), Nanostructure Science and Technology (2012). p. 73. [7] A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41.« less
  • One of the main limitations of imaging at high spatial and temporal resolution during in-situ transmission electron microscopy (TEM) experiments is the frame rate of the camera being used to image the dynamic process. While the recent development of direct detectors has provided the hardware to achieve frame rates approaching 0.1 ms, the cameras are expensive and must replace existing detectors. In this paper, we examine the use of coded aperture compressive sensing (CS) methods to increase the frame rate of any camera with simple, low-cost hardware modifications. The coded aperture approach allows multiple sub-frames to be coded and integratedmore » into a single camera frame during the acquisition process, and then extracted upon readout using statistical CS inversion. Here we describe the background of CS and statistical methods in depth and simulate the frame rates and efficiencies for in-situ TEM experiments. Depending on the resolution and signal/noise of the image, it should be possible to increase the speed of any camera by more than an order of magnitude using this approach.« less
  • Widespread use of {beta}-lactam antibiotics has promoted the evolution of {beta}-lactamase mutant enzymes that can hydrolyze ever newer classes of these drugs. Among the most pernicious mutants are the inhibitor-resistant TEM {beta}-lactamases (IRTs), which elude mechanism-based inhibitors, such as clavulanate. Despite much research on these IRTs, little is known about the structural bases of their action. This has made it difficult to understand how many of the resistance substitutions act as they often occur far from Ser-130. Here, three IRT structures, TEM-30 (R244S), TEM-32 (M69I/M182T), and TEM-34 (M69V), are determined by x-ray crystallography at 2.00, 1.61, and 1.52 {angstrom}, respectively.more » In TEM-30, the Arg-244 {yields} Ser substitution (7.8 {angstrom} from Ser-130) displaces a conserved water molecule that usually interacts with the {beta}-lactam C3 carboxylate. In TEM-32, the substitution Met-69 {yields} Ile (10 {angstrom} from Ser-130) appears to distort Ser-70, which in turn causes Ser-130 to adopt a new conformation, moving its O{gamma} further away, 2.3 {angstrom} from where the inhibitor would bind. This substitution also destabilizes the enzyme by 1.3 kcal/mol. The Met-182 {yields} Thr substitution (20 {angstrom} from Ser-130) has no effect on enzyme activity but rather restabilizes the enzyme by 2.9 kcal/mol. In TEM-34, the Met-69 {yields} Val substitution similarly leads to a conformational change in Ser-130, this time causing it to hydrogen bond with Lys-73 and Lys-234. This masks the lone pair electrons of Ser-130 O{gamma}, reducing its nucleophilicity for cross-linking. In these three structures, distant substitutions result in accommodations that converge on the same point of action, the local environment of Ser-130. TEM-1 {beta}-lactamase is the predominant source of resistance to {beta}-lactams, such as the penicillins. TEM-1 and related class A {beta}-lactamases confer resistance by hydrolyzing the {beta}-lactam ring of these antibiotics; bacteria expressing these enzymes have become widespread in hospitals and in the community. Beginning in 1980s, three mechanism-based class A {beta}-lactamase inhibitors, clavulanate, tazobactam, and sulbactam, have been used in combination with conventional penicillins to reverse this resistance (Fig.1, A-C). However, since 1992, more than 26 so-called inhibitor-resistant TEM (IRT)1 mutants have been selected, reversing susceptibility to these three mechanism-based inhibitors in the clinic (www.lahey.org/studies/temtable.stm).« less