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Article Abstract
Nanoscale hyperspectral techniques-such as electron energy loss spectroscopy (EELS)-are critical to understand the optical response in plasmonic nanostructures, but as systems become increasingly complex, the required sampling density and acquisition times become prohibitive for instrumental and specimen stability. As a result, there has been a recent push for new experimental methodologies that can provide comprehensive information about a complex system, while significantly reducing the duration of the experiment. Here, we present a pan-sharpening approach to hyperspectral EELS analysis, where we acquire two datasets from the same region (one with high spatial resolution and one with high spectral fidelity) and combine them to achieve a single dataset with the beneficial properties of both. This work outlines a straightforward, reproducible pathway to reduced experiment times and higher signal-to-noise ratios, while retaining the relevant physical parameters of the plasmonic response, and is generally applicable to a wide range of spectroscopy modalities.
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Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
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The acquisition of a hyperspectral image is nowadays a standard technique used in the scanning transmission electron microscope. It relates the spatial position of the electron probe to the spectral data associated with it. In the case of electron energy loss spectroscopy (EELS), frame-based hyperspectral acquisition is much slower than the achievable rastering time of the scan unit (SU), which sometimes leads to undesirable effects in the sample, such as electron irradiation damage, that goes unperceived during frame acquisition.
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Article Synopsis
- A novel machine learning approach is introduced to analyze and differentiate spectra within a large dataset from electron energy loss spectroscopy (EELS-SI).
- The method utilizes both linear and nonlinear dimensionality reduction techniques to effectively project the EEL spectra into a low-dimensional space, followed by a density-based clustering algorithm to identify distinct clusters of similar spectra.
- This technique allows for the investigation of specific fine structures within clusters and their spatial distributions in materials, making it applicable to any hyperspectral image without the need for prior knowledge.
Enhancing hyperspectral EELS analysis of complex plasmonic nanostructures with pan-sharpening.
J Chem Phys
January 2021
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
Nanoscale hyperspectral techniques-such as electron energy loss spectroscopy (EELS)-are critical to understand the optical response in plasmonic nanostructures, but as systems become increasingly complex, the required sampling density and acquisition times become prohibitive for instrumental and specimen stability. As a result, there has been a recent push for new experimental methodologies that can provide comprehensive information about a complex system, while significantly reducing the duration of the experiment. Here, we present a pan-sharpening approach to hyperspectral EELS analysis, where we acquire two datasets from the same region (one with high spatial resolution and one with high spectral fidelity) and combine them to achieve a single dataset with the beneficial properties of both.
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