Abstract
Plasmons in the visible/UV energy regime have attracted great attention, especially in nano-materials, with regards to applications in opto-electronics and light harvesting; tailored enhancement of such plasmons is of particular interest for prospects in nano-plasmonics. This work demonstrates that it is possible, by adequate doping, to create excitations in the visible/UV regime in nano-carbon materials, i.e., carbon nanotubes and graphene, with choice of suitable ad-atoms and dopants, which are introduced directly into the lattice by low energy ion implantation or added via deposition by evaporation. Investigations as to whether these excitations are of collective nature, i.e., have plasmonic character, are carried out via DFT calculations and experiment-based extraction of the dielectric function. They give evidence of collective excitation behaviour for a number of the introduced impurity species, including K, Ag, B, N, and Pd. It is furthermore demonstrated that such excitations can be concentrated at nano-features, e.g., along nano-holes in graphene through metal atoms adhering to the edges of these holes.
Highlights
Research in plasmonics has dramatically increased in recent years, with a number of suggested, possible applications, including biosensors, solar cells and sub-wavelength optics[1,2,3,4,5]
In order to investigate/consolidate dopant-introduced optical/near-UV plasmon behaviour in sheet-like carbon nanostructures, K, Na, Ba, Ag, N and B were introduced via low-energy ion implantation into few-walled carbon Carbon nanotubes (CNTs), primarily SWNTs and DWNTs, succeeded by energy loss spectroscopy (EELS) investigations
None of these contrast features can be observed in high resolution bright field electron microscopy (HREM) images of pristine CNTs
Summary
Spectroscopy in scanning mode tends to be the preferred way to acquire energy loss data. Energy filtered imaging (EFTEM) on the other hand provides fast acquisition of large-area energy loss images, making it is less destructive spectrum imaging, due to the lower beam currents, at the same time enabling sequential images (data cubes) to be taken with energy steps in the meV range. It requires, correction of non-isochromaticity and afterglow effects, procedures for which are explained in the results section. The RL algorithm is a maximum-likelihood Baysian deconvolution method that can be used to sharpen low-loss peaks and to remove the ZLP [e.g.29, p. 242]
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