Tailoring a Molecule’s Optical Absorbance Using Surface Plasmonics

Abstract

Understanding the interaction of light with molecules physisorbed on substrates is a fundamental problem in photonics, with applications in biosensing, photovoltaics, photocatalysis, plasmonics, and nanotechnology. However, the design of novel functional materials in silico is severely hampered by the lack of robust and computationally efficient methods for describing both molecular absorbance and screening on substrates. Here we employ our hybrid G 0 [W 0 + ∆W]-BSE implementation, which incorporates the substrate via its screening ∆W at both the quasiparticle G 0 W 0 level and when solving the Bethe-Salpeter equation (BSE). We show this method can be used to both efficiently and accurately describe the absorption spectra of physisorbed molecules on metal substrates and thereby tailor the molecule's absorbance by altering the surface plasmon's energy. Specifically, we investigate how the optical absorption spectra of three prototypical π-conjugated molecules: benzene (C 6 H 6 ), terrylene (C 30 H 16 ) and fullerene (C 60 ), depends on the Wigner-Seitz radius r s of the metallic substrate. To gain further understanding of the light-molecule/substrate interaction, we also study the bright exciton's electron and hole densities and their interactions with infrared active vibrational modes. Our results show that (1) benzene's bright E 1 1u exciton at 7.0 eV, whose energy is insensitive to changes in r s , could be relevant for photocatalytic dehydrogenation and polymerization reactions, (2) terrylene's bright B 3u exciton at 2.3 eV hybridizes with the surface plasmon, allowing the tailoring of the excitonic energy and optical activation of a surface plasmon-like exciton, and (3) fullerene's π − π * bright and dark excitons at 6.4 and 6.8 eV hybridize with the surface plasmon, resulting in the tailoring of their excitonic energy and the activation of both a surface plasmon-like exciton and a dark quadrupolar mode via symmetry breaking by the substrate. This work demonstrates how a proper description of interfacial light-molecular/substrate interactions enables the prediction, design, and optimization of technologically relevant phenomena in silico. arXiv:1910.05434v1 [cond-mat.mes-hall]

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].…”

“…Authors report an overall GPI lowering induced by the aggregation of molecules in the crystal. Mowbray et al [81] have on the other hand applied a self-developed G 0 (W 0 + ΔW)-BSE theoretical setup [214,215] to the study of the interaction of light with molecules (nominally, benzene C 6 H 6 , terrylene C 30 H 16 , and fullerene C 60 ) physisorbed on a metallic substrate. The methodology employed is multi-step, treating at first the isolated molecules (DFT first and then G 0 W 0 to get a reasonably good convergence for the bandgap of the molecules) and then their interactions with the surface.…”

“…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.…”

“…(2.4.11) and the third quadrupolar exciton mode ω 3 are marked. Reproduced with permission from Ref [81]…”