Publikationen

We analyze the coupling between double nanowire cavities for both photonic modes and plasmonic modes. When the spacing between nanowires reduces, a redshift of the resonant frequency of the symmetric mode and a blueshift of the resonant frequency of the antisymmetric mode are observed. Compared to single nanowire cavity modes, the Q factors of antisymmetric supermodes of double nanowires can be improved by 51% for photonic modes and by 24% for plasmonic modes. The mechanisms of Q factor improvement for photonic modes and plasmonic modes are studied based on the field distribution of radiations from the modes. This paper may contribute to research and applications for double nanowire lasers and nanowire laser arrays.

Epitaxially grown Fe nanostructures on Pb(111) were studied by low-temperature scanning tunneling microscopy and spectroscopy. The deposited Fe assemblies are classified into two groups according to their electronic behavior close to the Fermi energy. One group exhibits a wide energy gap of 0.7 eV that is independent of the temperature ranging from 5 K to room temperature. These Fe islands indicate the absence of the superconductivity proximity effect in their interior. The other group shows a metallic behavior at the Fermi level. The substrate superconducting phase locally enters into these islands, which is evidenced by a sharp resonance at the Fermi energy presumably signaling Andreev reflection at the magnet-superconductor interface.

In this paper, we study a quasilinear coupled magneto-quasistatic model from a systems theoretic perspective. First, by taking the injected voltages as input and the associated currents as output, we prove that the magneto-quasistatic system is passive. Moreover, by defining suitable Dirac and resistive structures, we show that it admits a representation as a port-Hamiltonian system. Thereafter, we consider dependence of the solution on initial and input data. We show that the current and the magnetic vector potential can be estimated by means of the initial magnetic vector potential and the voltage. We also analyse the free dynamics of the system and study the asymptotic behavior of the solutions for $ t\to\infty $.

Sensing and recognizing invisible ultraviolet (UV) light is vital for exploiting advanced artificial visual perception system. However, due to the uncertainty of the natural environment, the UV signal is very hard to be detected and perceived. Here, inspired by the tetrachromatic visual system, we report a controllable UV-ultrasensitive neuromorphic vision sensor (NeuVS) that uses organic phototransistors (OPTs) as the working unit to integrate sensing, memory and processing functions. Benefiting from asymmetric molecular structure and unique UV absorption of the active layer, the as fabricated UV-ultrasensitive NeuVS can detect 370 nm UV-light with the illumination intensity as low as 31 nW cm^-2, exhibiting one of the best optical figures of merit in UV-sensitive neuromorphic vision sensors. Furthermore, the NeuVS array exbibits good image sensing and memorization capability due to its ultrasensitive optical detection and large density of charge trapping states. In addition, the wavelength-selective response and multi-level optical memory properties are utilized to construct an artificial neural network for extract and identify the invisible UV information. The NeuVS array can perform static and dynamic image recognition from the original color image by filtering red, green and blue noise, and significantly improve the recognition accuracy from 46 to 90%.