Abstract
Technological advances in electron microscopy, particularly improved detectors and aberration correctors, have led to higher throughput and less invasive imaging of materials and biological structures by enhancing signal-to-noise ratios at lower electron exposures. Analytical methods, such as electron energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDS), have also benefitted and offer a rich set of local elemental and bonding information with nano-or atomic resolution. However, spatially resolved spectrum imaging with EELS and EDS continue to be difficult to scale due to limited detector collection angles or high signal background, requiring hours or even days for full maps. We present the principle and application of a Multi-Objective Autonomous Dynamic Sampling (MOADS) method which can accelerate spectrum mapping in EELS or EDS by over an order of magnitude. Initial guesses about the true spectrum images are constructed as measurements are collected, which allows the prediction of points which contribute information/contrast. In this fashion, an intelligently selected and reduced set of points which best approximate the true spectrum image are autonomously collected on-the-fly to save considerable time and/or radiative area dose.
Original language | English (US) |
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Pages (from-to) | 31-40 |
Number of pages | 10 |
Journal | Micron |
Volume | 108 |
DOIs | |
State | Published - May 2018 |
Funding
This work was supported by the NSF Graduate Research Fellowship Program and sponsored by the Air Force Research Laboratory under agreement number FA8650-15-2-5518 and the Air Force Office of Scientific Research under Award No. FA9550- 12-1-0280 . This work made use of the EPIC facility (NU ANCE Center-Northwestern University) and BioCryo facility (NU ANCE Center-Northwestern University), which has received support from the MRSEC program ( NSF DMR-1121262 ) at the Materials Research Center; the International Institute for Nanotechnology (IIN); the SHyNE Resource ( NSF NNCI-1542205 ); and the State of Illinois , through the IIN. The authors would like to acknowledge Dr. Bernhard Schaffer for assistance with Digital Micrograph scripting, Dr. Krishnamurthy Mahalingam for assistance operating EDS and EELS on FEI Titan, Caitlin Casar and Professor Maggie Osborn for the Gallionella sample, and Dr. Robert Wheeler for providing the Ti-Al sample. Appendix A This work was supported by the NSF Graduate Research Fellowship Program and sponsored by the Air Force Research Laboratory under agreement number FA8650-15-2-5518and the Air Force Office of Scientific Research under Award No. FA9550- 12-1-0280. This work made use of the EPIC facility (NUANCE Center-Northwestern University) and BioCryo facility (NUANCE Center-Northwestern University), which has received support from the MRSEC program (NSF DMR-1121262) at the Materials Research Center; the International Institute for Nanotechnology (IIN); the SHyNE Resource (NSF NNCI-1542205); and the State of Illinois, through the IIN. The authors would like to acknowledge Dr. Bernhard Schaffer for assistance with Digital Micrograph scripting, Dr. Krishnamurthy Mahalingam for assistance operating EDS and EELS on FEI Titan, Caitlin Casar and Professor Maggie Osborn for the Gallionella sample, and Dr. Robert Wheeler for providing the Ti-Al sample.
Keywords
- Dose reduction
- Dynamic sampling
- Electron energy loss spectroscopy
- Energy dispersive X-Ray spectroscopy
- Machine learning
- Scanning transmission electron microscopy
ASJC Scopus subject areas
- Structural Biology
- Cell Biology