Abstract
An accurate description of the optical response of subwavelength metallic particles and nanogap structures is a key problem of plasmonics. Quantum hydrodynamic theory (QHT) has emerged as a powerful method to calculate the optical response of metallic nanoparticles (NPs) since it takes into account nonlocality and spill-out effects. Nevertheless, the absorption spectra of metallic NPs obtained with conventional QHT, i.e., incorporating Thomas-Fermi (TF) and von Weizsäcker (vW) kinetic energy (KE) contributions, can be affected by several spurious resonances at energies higher than the main localized surface plasmon (LSP). These peaks are not present in reference time-dependent density-functional-theory spectra, where, instead, only a broad shoulder exists. Moreover, we show here that these peaks incorrectly reduce the LSP peak intensity and have a strong dependence on the simulation domain size so that a proper calculation of QHT absorption spectra can be problematic. In this article, we introduce a more general QHT method accounting for KE contributions depending on the Laplacian of the electronic density (q), thus, beyond the gradient-only dependence of the TFvW functional. We show that employing a KE functional with a term proportional to q2 results in an absorption spectrum free of spurious peaks, with LSP resonance of correct intensity and numerically stable Bennett state. Finally, we present a novel Laplacian-level KE functional that is very accurate for the description of the optical properties of NPs of different sizes as well as for dimers. Thus, the Laplacian-level QHT represents a novel, efficient, and accurate platform to study plasmonic systems.4 MoreReceived 5 June 2020Revised 21 October 2020Accepted 24 December 2020DOI:https://doi.org/10.1103/PhysRevX.11.011049Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDensity functional theoryPlasmonicsPlasmonsSurface plasmonsPhysical SystemsNanoparticlesTechniquesClassical electromagnetismDensity functional approximationsDensity functional calculationsFree-electron modelGGACondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical
Highlights
Metal nanoparticles (NPs) play a crucial role in the enhancement of the optical field due to plasmonic effects [1], which make them an ideal platform for nonlinear optics [2,3], hot-electron enhancement for photovoltaics [4,5], surface-enhanced Raman scattering [6], and imaging [7]
Quantum hydrodynamic theory (QHT) has emerged as a powerful method to calculate the optical response of metallic nanoparticles (NPs) since it takes into account nonlocality and spillout effects
We present a detailed comparison of the different kinetic energy (KE) functional for QHT, and we clearly demonstrate that the QHT-PGSLN approach is the most accurate and numerically stable method to treat plasmonics nanosystems
Summary
Metal nanoparticles (NPs) play a crucial role in the enhancement of the optical field due to plasmonic effects [1], which make them an ideal platform for nonlinear optics [2,3], hot-electron enhancement for photovoltaics [4,5], surface-enhanced Raman scattering [6], and imaging [7]. TD-DFT is computationally expensive since all occupied orbitals need to be evaluated Another approach would be to treat the electron system semiclassically: a fluid characterized by the macroscopic local quantities, such as the electron density nðr; tÞ and the electron velocity field vðr; tÞ [25,26,27,28], but at the same time considering quantum effects through energy functionals of the electron-density fluctuations. We perform calculations for Na jellium nanospheres (up to 6000 electrons) and demonstrate that in the QHT-PGSL approach, only the main LSP peak appears in the lower part of the absorption spectrum, which is stable to the changes of computational domain size as well as on the input density.
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