A proposed nanosensor based on hybrid nanoshells consisting of a core of metal nanoparticles and a coating of molecules is simulated by plasmon-exciton coupling in semi classical approach. We study the interaction of electromagnetic radiation with multilevel atoms in a way that takes into account both the spatial and the temporal dependence of the local fields. Our approach has a wide range of applications, from the description of pulse propagation in two-level media to the elaborate simulation of optoelectronic devices, including sensors. We have numerically solved the corresponding system of coupled Maxwell-Liouville equations using finite difference time domain (FDTD) method for different geometries. Plasmon-exciton hybrid nanoshells with different geometries are designed and simulated, which shows more sensitive to environment refractive index (RI) than nanosensor based on localized surface plasmon. The effects of nanoshell geometries, sizes, and quantum emitter parameters on the sensitivity of nanosensors to changes in the RI of the environment were investigated. It was found that the cone-like nanoshell with a silver core and quantum emitter shell had the highest sensitivity. The tapered shape of the cone like nanoshell leads to a higher density of plasmonic excitations at the tapered end of the nanoshell. Under specific conditions, two sharp, deep LSPR peaks were evident in the scattering data. These distinguishing features are valuable as signatures in nanosensors requiring fast, noninvasive response.
Graphene has received tremendous attention among diverse 2D materials because of its remarkable properties. Its emergence over the last two decades gave a new and distinct dynamic to the study of materials, with several research projects focusing on exploiting its intrinsic properties for optoelectronic devices. This review provides a comprehensive overview of several published articles based on density functional theory and recently introduced machine learning approaches applied to study the electronic and optical properties of graphene. A comprehensive catalogue of the bond lengths, band gaps, and formation energies of various doped graphene systems that determine thermodynamic stability was reported in the literature. In these studies, the peculiarity of the obtained results reported is consequent on the nature and type of the dopants, the choice of the XC functionals, the basis set, and the wrong input parameters. The different density functional theory models, as well as the strengths and uncertainties of the ML potentials employed in the machine learning approach to enhance the prediction models for graphene, were elucidated. Lastly, the thermal properties, modelling of graphene heterostructures, the superconducting behaviour of graphene, and optimization of the DFT models are grey areas that future studies should explore in enhancing its unique potential. Therefore, the identified future trends and knowledge gaps have a prospect in both academia and industry to design future and reliable optoelectronic devices.