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Electrically pumped distributed feedback (DFB) lasing in silicon nanocrystals (SiNCs) has been observed. The DFB SiNC laser was made based on a SiNC light-emitting diode (LED) by incorporating a second-order DFB grating. The light emission covered the visible regime. Optical gains in the order of 102 cm−1 were found in the light emitting regime. The DFB grating was so designed and fabricated that lasing wavelength in the red regime was selected. Signals emitting from the surface of the DFB SiNC laser were recorded. The laser was pumped by a pulsed direct-current power supply at room temperature. Four typical criteria of lasing were identified, i.e., 1) threshold behavior of the electroluminescence (EL) intensity versus pumping power density, 2) evident spectral narrowing of the EL spectrum, 3) well-recognized degree of polarization, and 4) small spread of light emitting angle. The latter three were for the pumping power density beyond the threshold one.

Abstract Unlike the single grating Czerny–Turner configuration spectrometers, a super-high spectral resolution optical spectrometer with zero coma aberration is first experimentally demonstrated by using a compound integrated diffraction grating module consisting of 44 high dispersion sub-gratings and a two-dimensional backside-illuminated charge-coupled device array photodetector. The demonstrated super-high resolution spectrometer gives 0.005 nm (5 pm) spectral resolution in ultra-violet range and 0.01 nm spectral resolution in the visible range, as well as a uniform efficiency of diffraction in a broad 200 nm to 1000 nm wavelength region. Our new zero-off-axis spectrometer configuration has the unique merit that enables it to be used for a wide range of spectral sensing and measurement applications.

A luminescent Si nanocrystal (SiNC) thin film with photoluminescence quantum yield (PLQY) > 70% was made by using hydrogen silsesquioxane (HSQ), followed by long-time high-pressure hydrogenation. A net optical gain of 524 ± 21 cm−1 was obtained by means of variable stripe length-shifting excitation spot (VSL-SES). A rectangularly shaped SiO2 Fabry-Perot (F-P) cavity with size of 1.5 μm (width) × 0.7 μm (height) × 2000 μm (length) was made on top of the SiNC thin film. The waveguide F-P device was pumped with a 400 nm femtosecond pulsed laser. A threshold behavior of the light emission intensity as a function of the pumping power was observed. Other lasing characteristics including spectral narrowing, polarization of the emission, and small emission angle were also observed beyond the threshold pumping power.

It is of great technological importance in the field of plasmonic color generation to establish and understand the relationship between optical responses and the reflectance of metallic nanoparticles. Previously, a series of indium nanoparticle ensembles were fabricated using electron beam evaporation and inspected using spectroscopic ellipsometry (SE). The multi-oscillator Lorentz–Drude model demonstrated the optical responses of indium nanoparticles with different sizes and size distributions. The reflectance spectra and colorimetry characteristics of indium nanoparticles with unimodal and bimodal size distributions were interpreted based on the SE analysis. The trends of reflectance spectra were explained by the transfer matrix method. The effects of optical constants n and k of indium on the reflectance were demonstrated by mapping the reflectance contour lines on the n-k plane. Using oscillator decomposition, the influence of different electron behaviors in various indium structures on the reflectance spectra was revealed intuitively. The contribution of each oscillator on the colorimetry characteristics, including hue, lightness and saturation, were determined and discussed from the reflectance spectral analysis.

Abstract The optical properties of aluminum-doped zinc oxide (AZO) thin films were calculated rapidly and accurately by point-by-point analysis from spectroscopic ellipsometry (SE) data. It was demonstrated that there were two different physical mechanisms, i.e., the interfacial effect and crystallinity, for the thickness-dependent permittivity in the visible and infrared regions. In addition, there was a blue shift for the effective plasma frequency of AZO when the thickness increased, and the effective plasma frequency did not exist for AZO ultrathin films (

In this work, we present a simple method to improve the thermoelectric performance of the RF sputtered bismuth telluride films by raising the power of deposition. The as-deposited samples synthesized under different powers were investigated and compared. It shows that the films prepared under relatively higher power conditions exhibit much higher electrical conductivity to result in a greater power factor accompanied with a minor drop in the Seebeck coefficients. A relationship is established between the improvement in thermoelectric performance and the decrease in crystallinity, which might also reduce the thermal conductivity. A maximum power factor of 5.65 × 10−4 W·m−1·K−2 at 470 K is obtained for the sample deposited under 50 W with its Seebeck coefficient being −105 μV/K. The temperature-dependent behaviors of the samples are also looked into and discussed. This work might offer an in-situ and cost-effective approach to improve the performance of thermoelectric materials.

Graph is an important and fundamental data structure that presents in a wide variety of practical scenarios.With the rapid development of the Internet in recent years,there has been a huge increase in social network graph data,and the analysis of this data can be of great help in practical scenarios such as public services and advertising and marketing.There are already quite a few graph neural network algorithms that can get good results in such problems,but they still have room for improvement,and in many scenarios where high accuracy is pursued,engineers still want to have algorithms with better performance to choose from.This paper improves personalized propagation of neural predictions and proposes a new graph neural network algorithm called degree of node based personalized propagation of neural predictions(DPPNP)that can be used in social graph networks.Compared to traditional graph neural network algorithms,when the information is propagated between nodes,the proposed algorithm will keep its own information for different nodes in different proportions according to the degree of nodes,so as to improve the accuracy.Experiments on real datasets show that the proposed algorithm has better performance compared to previous graph neural network algorithms.