We report the threshold emission characteristics of lasing from microdroplets that consist of Rhodamine 6G dye in an ethanol solution that contains undoped polystyrene latex spheres. The addition of latex particles to the droplets suppresses lasing. Our findings indicate that, for a fixed Rhodamine 6G concentration and fixed pump intensity, lasing ceases when a certain total-threshold latex particle surface area is reached in the droplet, independent of latex particle size. A possible explanation for these findings is the Förster-assisted annihilation of Rhodamine 6G dye lasing levels, facilitated by the adsorption of dye molecules on the surfaces of latex particles.
Stimulated Raman scattering (SRS) spectra from micrometer-sized water droplets have been obtained in the range 2100 < Δν < 5100 cm-(1). A number of Raman bands have been individually identified (to our knowledge, for the first time), corresponding to fundamental OH- and OD-stretching vibrations and to vibrations of hydrogen-bonded molecular complexes. All bands exhibit the intense morphologydependent resonance features that are characteristic of SRS emission from microdroplets. SRS emission is apparently random from all bands; however, the frequency of occurrence varies widely, from bands where emission is seen on practically every laser shot to bands where emission is seen only once in > 10(4) laser shots. Possible causes of these noteworthy emission features are discussed, including the difficulty of coupling weak spontaneous Raman emission to both the intense pump beam and the morphologydependent resonances within the droplet.
Stimulated Raman scattering (SRS) from micrometer-sized droplets, which results from coupling of spontaneous Raman emission to droplet morphology-dependent resonances (MDR's), exhibits unique characteristics. Spatial patterns consist of bright SRS arcs on the droplet rim. A second source of SRS emission has recently been observed from a ringlike region encircling the droplet axis near the geometric focus (the Descartes ring). Investigation of the time and spectral characteristics of Descartes ring SRS and its suppression by the addition of absorptive dye to the droplet reveals it to be an additional manifestation of droplet MDR's. We conjecture that the Descartes ring results when the MDR light is scattered by refractive-index inhomogeneities produced by the intense pump field within the droplet.
Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.
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