Chiral metallic nanoparticles can exhibit novel plasmonic circular dichroism (PCD) in the ultraviolet and visible range of the electromagnetic spectrum. Here, we investigate how thermoresponsive dielectric nanoenvironments will influence such...

Two-dimensional (2D) plasmonic nanoassemblies are programmable ultrathin materials that one can, in principle, adjust the size and shape of their constituent building blocks to fine-tune collective optical, electrical, and mechanical properties. Here, we report a new 2D nanoassembly from structurally complex plasmonic building blocks, namely, matryoshka-like gold (Au) nanoframes. Using a seed-mediated alternating deposition of Au and silver (Ag) elements in conjunction with a selective etching process, we obtain monodisperse matryoshka-like Au nanoframes with a nesting number (N n) of up to 5. A cubic nanoframe displays dominant intraplasmonic coupling attributed to bonding and antibonding dipolar modes, which shift to blue and red, respectively, with an increased N n. Combined with polystyrene (PS) ligand exchange and drying-mediated self-assembly, the approach mentioned above can be used to produce 2D plasmonic matryoshka nanoassemblies. Both experimental and simulation results demonstrate the presence of intra- and inter-ridge plasmonic coupling in the nanoassemblies. The 5-nested nanomatryoshka assemblies exhibit a Raman enhancement 14-fold greater than those with 1-nested cubic nanomatryoshka, demonstrating the dominant intraridge hot spot effects. Taking advantage of interparticle distance-dependent optical transparency of 2D matryoshka nanoframe assemblies, we further demonstrate temperature-enabled encryption/decryption using thermoresponsive polymers.

Plasmonic nanoshells show great promise in a wide range of quantum applications due to the tunability of the plasmon frequency across a broad spectral range. With the aim of giving a clear detailed analysis of the onset of plasmon generation and subsequent sustenance of plasmons on nanoshells in the quantum limit, we provide a fully quantum mechanical description of nanoshells using real-time and real-space time-dependent density functional theory. On the basis of the aspect ratio (AR), we identify three types of nanoshells; thin (AR ≥ 0.6), medium (0.35 ≤ AR < 0.6), and thick (AR < 0.35), each showing different types of plasmon modes. Small nanoshells with a thin metallic layer show symmetrically or antisymmetrically coupled plasmons, which agree with semiclassical predictions. However, in nanoshells with medium and thick metallic layers, we identify two types of quantum core plasmons modes, each mixing with symmetrically or antisymmetrically coupled plasmons. For semiclassical modes, all of the electron transitions; monotonic and oscillatory, are near the Fermi level, whereas for quantum core plasmon modes, electrons tend to transition to higher energy levels. We also discuss the dependency of optical properties on aspect ratio, overall size, ground-state electron density profile, spillout, material, and symmetry of nanoshells.

Mie–Gans theory optically characterizes ellipsoidal and by extension generally elongated nonchiral metal nanoparticles (MNPs) and is ubiquitous in verifying experimental results and predicting particle behavior. Recently, elongated chiral MNPs have garnered enthusiasm, but a theory to characterize their chiroptical behavior is lacking in the literature. In this Letter, we present an ab initio model for chiral ellipsoidal MNPs to address this shortcoming and demonstrate that it reduces to the general Mie–Gans model under nonchiral conditions, produces results that concur with state-of-the-art numerical simulations, and can accurately replicate recent experimental measurements. Furthermore, to gain physical insights, we analyze factors such as background medium permittivity and particle size that drive the chiroptical activity using two types of plasmonic chiral MNPs. We also demonstrate the utility of our model in metamaterial design. Generic features of our model can be extended to characterize similar elongated chiral MNPs, fueling many other variants of the current model.

Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex networks. In particular, we consider a network with pairwise-fixed communication links. Corresponding combinatorial optimization is a non-deterministic polynomial (NP)-hard without a closed-form solution. In this respect, the existing heuristics entail high computational complexity, raising a scalability issue in large networks. Motivated by the recent success of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net, to jointly optimize the communication direction and transmission power. The proposed GNN produces near-optimal performance meanwhile maintaining a low computational complexity compared to the most commonly used techniques. Furthermore, our numerical results shed light on the advantages of using GNNs in terms of sample complexity, scalability, and generalization capability.