The interaction of silicon (Si) nanospheres (NSs) with a thin metal film is investigated numerically and experimentally by characterizing their forward scattering properties. A sharp resonant mode and a zero-scattering dip are found to be introduced in the forward scattering spectrum of a Si NS by putting it on a 50-nm-thick gold film. It is revealed that the sharp resonant mode arises from a new magnetic dipole induced by the electric dipole and its mirror image while the zero-scattering dip originates from the destructive interference between the new magnetic dipole and the original one together with its mirror image. A significant enhancement in both electric and magnetic fields is achieved at the contact point between the Si NS and the metal film. More interestingly, the use of a thin silver film can lead to vivid scattering light with different color indices. It is demonstrated that a small change in the surrounding environment of Si NSs results in the broadening of the resonant mode and the disappearance of the zero-scattering dip. Our findings indicate that dielectric-metal hybrid systems composed of semiconductor NSs and thin metal films act as attractive platforms on which novel nanoscale plasmonic devices can be realized.

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When studying stochastic gene transcription, it is important to understand how system parameters are temporally modulated in response to varying environments. Experimentally, the dynamic distribution data of RNA copy numbers measured at multiple time points are often fitted to stochastic transcription models to estimate time-dependent parameters. However, current methods require determining which parameters are time-dependent, as well as their analytical formulas, before the optimal fit. In this study, we developed a method to estimate time-dependent parameters in a classical two-state model without prior assumptions regarding the system parameters. At each measured time point, the method fitted the dynamic distribution data using a steady-state distribution formula, in which the estimated constant parameters were approximated as time-dependent parameter values at the measured time point. The accuracy of this method can be guaranteed for RNA molecules with relatively high degradation rates and genes with relatively slow responses to induction. Furthermore, we implemented this method on two sets of dynamic distribution data from prokaryotic and eukaryotic cells, and revealed the temporal modulation of transcription burst size in response to environmental changes.