Ionic liquid based, ultrasound-assisted extraction was successfully applied to the extraction of phenolcarboxylic acids, carnosic acid and rosmarinic acid, from Rosmarinus officinalis. Eight ionic liquids, with different cations and anions, were investigated in this work and [C8mim]Br was selected as the optimal solvent. Ultrasound extraction parameters, including soaking time, solid–liquid ratio, ultrasound power and time, and the number of extraction cycles, were discussed by single factor experiments and the main influence factors were optimized by response surface methodology. The proposed approach was demonstrated as having higher efficiency, shorter extraction time and as a new alternative for the extraction of carnosic acid and rosmarinic acid from R. officinalis compared with traditional reference extraction methods. Ionic liquids are considered to be green solvents, in the ultrasound-assisted extraction of key chemicals from medicinal plants, and show great potential.

Respirable particulate matter air pollution is positively associated with SARS-CoV-2 mortality. Real-time and accurate monitoring of particle concentration changes is the first step to prevent and control air pollution from inhalable particles. In this research, a new light scattering instrument has been developed to detect the mass concentration of inhalable particles. This instrument couples the forward small-angle single particle counting method with the lateral group particle photometry method in a single device. The mass concentration of four sizes of inhalable particles in the environment can be detected simultaneously in a large area in real-time without using a particle impactor. Different from the traditional light scattering instrument, this new optical instrument can detect darker particles with strong light absorption, and the measurement results mainly depend on the particle size and ignore the properties of the particles. Comparative experiments have shown that the instrument can detect particles with different properties by simply calibrating the environmental density parameters, and the measurement results have good stability and accuracy.

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Inhalable particulate matter has been widely concerned due to its serious harm to human health in China. Real time, rapid and high-resolution monitoring of particle concentration change is the first step to prevent and control inhalable particle pollution. In this paper we present a method for detecting fine particle mass concentration based on forward and lateral light scattering measurement. Based on Mie light scattering theory, we design and establish the experimental platform of multi-angle light scattering measurement. Moreover, a portable multi-angle light scattering detection particle mass concentration prototype is developed by using computer modeling, 3D printing and weak photoelectric signal detection technology. Through theoretical numerical simulation and experimental analysis of optical platform, 20° forward and 45° lateral are selected as the optimal detection angles with the advantages of simple structure and high efficiency. Finally, we obtain the pulse reference voltage of different particle size segments is to realize the particle size segment detection. A specific case of our nonlinear regression algorithm is used to calibrate parameters of the detection system. The feasibility of the proposed detection method is verified by the comparative detection experiments in the laboratory and the outdoor atmospheric environment.

The small-angle optical particle counter (OPC) can detect particles with strong light absorption. At the same time, it can ignore the properties of the detected particles and detect the particle size singly and more accurately. Reasonably improving the resolution of the low pulse signal of fine particles is key to improving the detection accuracy of the small-angle OPC. In this paper, a new adaptive filtering method for the small-angle scattering signals of particles is proposed based on the recursive least squares (RLS) algorithm. By analyzing the characteristics of the small-angle scattering signals, a variable forgetting factor (VFF) strategy is introduced to optimize the forgetting factor in the traditional RLS algorithm. It can distinguish the scattering signal from the stray light signal and dynamically adapt to the change in pulse amplitude according to different light absorptions and different particle sizes. To verify the filtering effect, small-angle scattering pulse extraction experiments were carried out in a simulated smoke box with different particle properties. The experiments show that the proposed VFF-RLS algorithm can effectively suppress system stray light and background noise. When the particle detection signal appears, the algorithm has fast convergence and tracking speed and highlights the particle pulse signal well. Compared with that of the traditional scattering pulse extraction method, the resolution of the processed scattering pulse signal of particles is greatly improved, and the extraction of weak particle scattering pulses at a small angle has a greater advantage. Finally, the effect of filter order in the algorithm on the results of extracting scattering pulses is discussed.