Formation of normal surface plasmon modes in small sodium nanoparticles

Abstract

The work is devoted to the mechanism of the formation and conditions of existence of surface plasmon polaritons in small nanoparticles.

Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond

“…We therefore specifically highlighted, besides these well-known methods, emerging approaches which have the potential to significantly enhance modeling capabilities for plasmonics in the coming years, in particular, by allowing modeling at realistic length scales. These methods include time-dependent orbital-free DFT [85,86], linear response and real-time time-dependent density functional tight binding [87][88][89][90] and machine learning based approaches, as well as modeling beyond the single-particle based (DFT) picture with many-body perturbation theory [78][79][80][81].…”

“…In other words, localized surface plasmons and volume plasmons will 'communicate' differently with the perturbation, with the latter activated via the communication with the former. GW in this sense helps in solving this shortcoming [78].…”

“…More recently, Matsko has investigated with GW the formation of normal surface plasmon modes in sodium NPs [78]. The work aims to provide theoretical insight which is especially important in view of the difficulties with experimental studies of normal SPP data for small NPs.…”

“…For this reason, real-time TD-DFT [77] has found wider use in plasmonics but still suffers from the high cost of Kohn-Sham DFT. The scale also poses serious cost hurdles for the application of approaches including many body effects [78][79][80][81]. It is therefore important to consider novel avenues for atomistic modeling which are suited for the modeling of plasmonics.…”

“…While some more well-known approaches such as classical and TD-DFT were previously reviewed [82][83][84], the bulk of DFT-based literature deals with system sizes limited to a few nm, which is on the lower part of the range of particle sizes used in PV. We therefore specifically highlight emerging approaches which have the potential to significantly enhance modeling capabilities in the coming years, in particular, by allowing modeling at realistic length scales (of particles beyond 10 nm which are of most interest to plasmonic solar cells but remain problematic with Kohn-Sham DFT), such as timedependent orbital-free DFT [85,86], linear response and real-time time-dependent density functional tight binding [87][88][89][90] and machine learning-based approaches, and by allowing modeling beyond the single-particle based (DFT) picture with many-body perturbation theory [78][79][80][81]. The latter approach, while being computationally expensive, is also finding increasing use in the modeling of plasmonics as access to computing power expands, and is therefore explicitly included in this review.…”