Unified Theoretical Model of Loss Compensation and Energy Transfer for Plasmonic Nanoparticles Coated with a Shell of Active Gain Molecules

Abstract

One issue in using metallic nanostructures for metamaterial applications at optical frequencies is their high level of losses. A most promising strategy to circumvent this obstacle is loss compensation, where the structures are coupled to active compounds enabled to transfer energy and therefore amplify the desired response. We here present the first unified theory of the response of plasmonic nanoparticles assisted by optical gain media, in the case of a nanoparticle coated with a shell of optically active dipoles (fluorescent molecules or dyes). The mechanism of the losses compensation is based on nonradiative energy transfer (ET) or quenching between the layer of gain elements and nanoparticle [1, 2]. We establish a complete description of the optical response of the system based on Green’s functions, which allows us to investigate high molecular coverage of nanoparticle with either regular or random distribution of dye molecules, taking into account not only the interactions between NP (treated in a multipolar approach) and dye dipoles, but also between dyes molecules, either directly or via the nanoparticle [3–5]. We then obtain the optical response of the core-shell aggregate in terms of its equivalent polarizability composed of the direct response from the nanoparticle and the contribution rising from the energy transfer mechanism. Our numerical calculations reveals that cooperative plasmon-mediated coupling between optically active dyes and metal nanostructure leads to the compensation of plasmon losses and some instability that is resolved either by surface plasmon amplification of stimulated emission (spasing states) or by enhanced absorption in the system.KeywordsLoss CompensationPlasmonic NanoparticlesUnified Theoretical ModelSurface Plasmon AmplificationOptical Gain MediumThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.