ORCID Profile
0000-0002-8737-451X
Current Organisation
The University of Texas at Austin
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Publisher: AIP Publishing
Date: 04-01-2021
DOI: 10.1063/5.0031324
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 s ling 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.
Publisher: Wiley
Date: 19-04-2021
Abstract: Electron energy loss spectroscopy (EELS) enables direct exploration of plasmonic phenomena at the nanometer level. To isolate in idual plasmon modes, linear unmixing methods can be used to separate different physical mechanisms, but in larger and more complex systems the interpretability of the components becomes uncertain. Here, infrared plasmonic resonances in self‐assembled heterogeneous monolayer films of doped‐semiconductor nanoparticles are examined beyond linear unmixing techniques, and both supervised and unsupervised machine‐learning‐based analyses of hyperspectral EELS datasets are demonstrated. In the supervised approach, a human operator labels a small number of pixels in the hyperspectral dataset corresponding to features of interest which are then propagated across the entire dataset. In the unsupervised approach, non‐linear autoencoders are used to create a highly‐reduced latent‐space representation of the dataset, within which insight into the relevant physics can be gleaned from straightforward distance metrics that do not depend on operator input and bias. The advantage of these approaches is that the labeling separates physical mechanisms without altering the data, enabling robust analyses of the influence of heterogeneities in mesoscale complex systems.
Location: United States of America
Location: United States of America
Location: United States of America
Location: United States of America
No related grants have been discovered for Delia Milliron.