Strong photoluminescence with sub-band-gap photon energies has been observed in fine Si particles prepared by the gas-evaporation technique. After surface oxidation, the Si particles show above-band-gap photoluminescence, the band tail covering the visible light region. The amount of the increased apparent band gap (0.3 eV) estimated from this blueshift can be explained by a quantum-size effect expected to be observed in Si quantum dots with a diameter of 50 Å.

A photoluminescence study has been made on an oxygen-containing Si fine structure fabricated by a gas evaporation technique. Transmission electron micrographs have shown that the fine structure is composed of nonspherical particles aggregated together in chain-like or cluster-like structures. The luminescence from the samples after oxidation treatment is bright blue as viewed with the naked eye, the spectra having a peak at about 470 nm or shorter wavelength. A peculiar temperature dependence of the emission peak indicates that the emission is strongly correlated with some structural change in the fine structure.

Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change in grapes’ quality parameters during ripening, a rapid and nondestructive method of visible–near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes in four different ripening stages were explored, with increasing color in redness/greenness (a*) and Chroma (C*) as well as soluble solids (SSC) content as ripening advanced, and decreasing values in color of lightness (L*), yellowness/blueness (b*), hue angle (h*), hardness, and total acid (TA) content. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected using the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectral data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The models of SSC and TA with full spectral data using PLSR after 1st derivative preprocessing both obtained the best results. For SSC, these yielded optimum results when the coefficients of determination of PLSR models for the calibration set (RCal2) and the prediction set (RPre2) were 0.97 and 0.93, respectively; the root mean square error for the calibration set (RMSEC) and the prediction set (RMSEP) were 0.62% and 1.27%, respectively; and the RPD was 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP, and RPD were 0.97, 0.94, 0.88 g/L, 1.96 g/L, and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.