The interaction of atmospheric aerosols with radiation remains a significant source of uncertainty in modeling radiative forcing. Laboratory measurements of the microphysical properties of atmospherically relevant particles is one approach to reduce this uncertainty. We report a new method to investigate light absorption by a single aerosol particle, inferring changes in the imaginary part of the refractive index with a change in environmental conditions (e.g., relative humidity) and inferring the size dependence of the optical extinction cross section. More specifically, we present measurements of the response of single aerosol particles to near-infrared (NIR) laser-induced heating at a wavelength of 1520 nm. Particles were composed of aqueous NaCl or (NH)SO and were studied over ranges in relative humidity (40-85%), particle radius (1-2.2 μm), and NIR laser power. The ensuing size change and real component of the refractive index were extracted from measurements of the angular variation in elastically scattered light. From the heating-induced size change at varying NIR beam intensities, we retrieved the change in the imaginary component of the refractive index. In addition, cavity ring-down spectroscopy measurements monitored the change in extinction cross section with modulation of the heating laser power.

We present a relatively detailed analysis of the persistence probability distributions in financial dynamics. Compared with the auto-correlation function, the persistence probability distributions describe dynamic correlations non-local in time. Universal and non-universal behaviors of the German DAX and Shanghai Index are analyzed, and numerical simulations of some microscopic models are also performed. Around the fixed point z0 = 0, the interacting herding model produces the scaling behavior of the real markets.

A multi-channel light emitting diode (LED)-induced fluorescence system combined with a convolutional neural network (CNN) analytical method was proposed to classify the varieties of tea leaves. The fluorescence system was developed employing seven LEDs with spectra ranging from ultra-violet (UV) to blue as excitation light sources. The LEDs were lit up sequentially to induce a respective fluorescence spectrum, and their ability to excite fluorescence from components in tea leaves were investigated. All the spectral data were merged together to form a two-dimensional matrix and processed by a CNN model, which is famous for its strong ability in pattern recognition. Principal component analysis combined with k-nearest-neighbor classification was also employed as a baseline for comparison. Six grades of green tea, two types of black tea and one kind of white tea were verified. The result proved a significant improvement in accuracy and showed that the proposed system and methodology provides a fast, compact and robust approach for tea classification.

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We demonstrate a second-harmonic-generation (SHG) based method for the detection of gaseous elemental mercury by using a newly available green diode laser. Multimode ultraviolet radiation at 253.7 nm is generated through a process of SHG. Correlation spectroscopy is introduced into the scheme to guarantee the measurement accuracy. The limit of detection achieved is 0.6 μg/m³ (0.07 ppb) for 1-m pathlength and 10-s integration time. The measurement accuracy is estimated to be 1.2%. The linear response range is estimated to be 0~60 μg/m² (6.7 ppb·m), within which the linearity error is less than 1%. Real-time monitoring of mercury volatilization is demonstrated with a time resolution of 1 s. The results of performance characterization show that the proposed method has great potentials for mercury sensing in environmental and industrial fields.