Toward laser-induced tuning of plasmonic response in high aspect ratio gold nanostructures

Peláez-Fernández, Mario; Majérus, Bruno; Dufour, Romain; Duvail, Jean-Luc; Henrard, Luc; Arenal, Raúl

doi:10.1515/nanoph-2022-0193

Cited by 4 publications

(2 citation statements)

References 52 publications

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“…Likewise, a shorter hollow nanotube would reveal the presence of a lower number of Fabry–Perot resonator modes. The fact that hollow 1D nanostructures have highly intense Fabry–Perot and LSPR modes at the inner and outer parts of the nanotube suggests that they can be good alternatives in different applications of plasmonic nanoantennas [ 11 , 12 , 14 , 17 ].…”

Section: Resultsmentioning

confidence: 99%

“…Plasmonic nanostructures can be defined as nanoantennas as their EM modulation mechanism is similar to the radio antennas [ 11 , 12 ]. Gold and silver nanostructures are promising nanoantennas with good metallic properties [ 14 , 17 ]. Due to their ability to convert light into highly localized fields, plasmonic nanoantennas have been used to enhance the performance of various photoactive devices, such as solar cells, photodetectors, and biosensors [ 7 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes

et al. 2023

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Morphological control at the nanoscale paves the way to fabricate nanostructures with desired plasmonic properties. In this study, we discuss the nanoengineering of plasmon resonances in 1D hollow nanostructures of two different AuAg nanotubes, including completely hollow nanotubes and hybrid nanotubes with solid Ag and hollow AuAg segments. Spatially resolved plasmon mapping by electron energy loss spectroscopy (EELS) revealed the presence of high order resonator-like modes and localized surface plasmon resonance (LSPR) modes in both nanotubes. The experimental findings accurately correlated with the boundary element method (BEM) simulations. Both experiments and simulations revealed that the plasmon resonances are intensely present inside the nanotubes due to plasmon hybridization. Based on the experimental and simulated results, we show that the novel hybrid AuAg nanotubes possess two significant coexisting features: (i) LSPRs are distinctively generated from the hollow and solid parts of the hybrid AuAg nanotube, which creates a way to control a broad range of plasmon resonances with one single nanostructure, and (ii) the periodicity of the high-order modes are disrupted due to the plasmon hybridization by the interaction of solid and hollow parts, resulting in an asymmetrical plasmon distribution in 1D nanostructures. The asymmetry could be modulated/engineered to control the coded plasmonic nanotubes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes

et al. 2023

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Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks

et al. 2023

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Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks

del-Pozo-Bueno,

Kepaptsoglou,

Ramasse

et al. 2024

Microscopy and Microanalysis

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Recent advances in machine learning (ML) have highlighted a novel challenge concerning the quality and quantity of data required to effectively train algorithms in supervised ML procedures. This article introduces a data augmentation (DA) strategy for electron energy loss spectroscopy (EELS) data, employing generative adversarial networks (GANs). We present an innovative approach, called the data augmentation generative adversarial network (DAG), which facilitates data generation from a very limited number of spectra, around 100. Throughout this study, we explore the optimal configuration for GANs to produce realistic spectra. Notably, our DAG generates realistic spectra, and the spectra produced by the generator are successfully used in real-world applications to train classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) that have been successful in classifying experimental EEL spectra.

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Toward laser-induced tuning of plasmonic response in high aspect ratio gold nanostructures

Cited by 4 publications

References 52 publications

Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes

Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes

Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks

Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks

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