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DOI 10.1002/smtd.71125

Медицинская библиотека · Small methods · 2026

Energy-Resolved Localized Surface Plasmon Resonances With Non-Monochromated STEM-EELS Assisted by Multivariate Analysis.

Catingan Sara D, He Wanting, Destefani Carlos F, Valdez Jesus, Besteiro Lucas V, Moores Audrey

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Аннотация

Localized surface plasmon resonances (LSPRs) govern the optical response of plasmonic nanomaterials and are central to applications in sensing, spectroscopy, therapeutics, and photocatalysis. Nanoscale mapping of low-energy plasmon modes can be characterized using scanning transmission electron microscopy/electron energy-loss spectroscopy (STEM-EELS). However, achieving sufficient energy resolution to extract low-energy LSPR signals typically requires advanced instrumentation, such as a monochromator and/or a cold-field emission gun. Here, an accessible STEM-EELS approach is developed for resolving LSPRs using a conventional non-monochromated TEM. Silica-coated gold nanorods (Au@SiO2 NRs) are investigated using low-dose, low-keV STEM-EELS combined with Richardson-Lucy deconvolution and principal component analysis, reducing the average full width at half maximum of the zero-loss peak to 0.46 eV and enabling the resolution of closely spaced LSPRs without a monochromator or other specialized hardware. Successful resolution of the longitudinal and transverse LSPR modes enables the visualization of the electromagnetic field distribution around the Au@SiO2 NRs. Three-dimensional electron tomography and finite element method simulations further validate the experimentally resolved plasmon distributions and field localization. This work establishes a broadly accessible strategy for high energy resolution plasmon mapping using standard STEM instrumentation, extending the capabilities of conventional electron microscopy platforms.

Для цитирования: Catingan Sara D, He Wanting, Destefani Carlos F, Valdez Jesus, Besteiro Lucas V, Moores Audrey. Energy-Resolved Localized Surface Plasmon Resonances With Non-Monochromated STEM-EELS Assisted by Multivariate Analysis.. Small methods. 2026. DOI: 10.1002/smtd.71125.
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