Influence of molecular structure on the coupling strength to a plasmonic nanoparticle and hot carrier generation.

Strong coupling between metal nanoparticles and molecules mixes their excitations, creating new eigenstates with modified properties such as altered chemical reactivity, different relaxation pathways or modified phase transitions. Here, we explore excited state plasmon-molecule coupling and discuss how strong coupling together with a changed orientation and number of an asymmetric molecule affects the generation of hot carriers in the system. We used a promising plasmonic material, magnesium, for the nanoparticle and coupled it with CPDT molecules, which are used in organic optoelectronic materials for organic electronic applications due to their facile modification, electron-rich structure, low band gap, high electrical conductivity and good charge transport properties. By employing computational quantum electronic tools we demonstrate the existence of a strong coupling mediated charge transfer plasmon whose direction, magnitude, and spectral position can be tuned. We find that the orientation of CPDT changes the nanoparticle-molecule gap for which maximum charge separation occurs, while larger gaps result in trapping hot carriers within the moieties due to weaker interactions. This research highlights the potential for tuning hot carrier generation in strongly coupled plasmon-molecule systems for enhanced energy generation or excited state chemistry.

Far-field signature of sub-wavelength microscopic objects.

Information about microscopic objects with features smaller than the diffraction limit is almost entirely lost in a far-field diffraction image but could be partly recovered with data completition techniques. Any such approach critically depends on the level of noise. This new path to superresolution has been recently investigated with use of compressed sensing and machine learning. We demonstrate a two-stage technique based on deconvolution and genetic optimization which enables the recovery of objects with features of 1/10 of the wavelength. We indicate that l1-norm based optimization in the Fourier domain unrelated to sparsity is more robust to noise than its l2-based counterpart. We also introduce an extremely fast general purpose restricted domain calculation method for Fourier transform based iterative algorithms operating on sparse data.