Raman spectroscopy is widely utilized in multi-component gas detection due to its rapid detection, good repeatability, and low cross-interference. However, the inherently low Raman scattering cross-section of gases results in exceedingly weak Raman signals, which impedes the further advancement of Raman spectroscopy gas detection. The multi-pass cavity-enhanced Raman spectroscopy technique can improve the excitation efficiency of Raman signals by converging the laser through multiple reflections to a focal point. Nonetheless, the employment of lenses to capture Raman signals at the convergence point results in limited collection efficiency. To address this challenge, Parabolic Mirror Cavity-Enhanced Raman Spectroscopy (PMCERS) is proposed aimed at concurrently enhancing the excitation and collection efficiency of Raman signal light. A parabolic mirror collection cavity is introduced to precisely couple the focal point of the parabolic mirror with the central convergence point of the multi-pass cavity, thereby collimating the Raman signal light at the focal point into parallel light emission. To enable efficient collimation of signal light emissions while preserving the integrity of signal collection efficiency, an annular reflector was integrated with the parabolic mirror. Through the analysis of the angles at which light rays were emitted from the focal point, the ideal dimensions of the annular reflector were ascertained. To verify the effectiveness of the PMCERS, comparative experiments demonstrated that, compared to traditional near-concentric cavity, PMCERS increased the Raman signal intensity detected in air by five times. In all, PMCERS enhanced both signal excitation and collection efficiency while simplifying the system structure, providing an efficient and stable solution for multi-component gas detection.
Aerosols consist of solid or liquid particles suspended in a gaseous medium. Distinct aerosol particles exhibit unique absorption and scattering spectra, thereby influencing the absorption and scattering of solar radiation. These processes contribute to warming the surrounding atmosphere and cooling the Earth's surface, subsequently impacting convective processes and cloud properties. Various types of aerosols are introduced into the atmosphere through anthropogenic and natural processes, exerting an influence on climate via direct or indirect radiative forcing. Accurately measuring aerosol optical properties, particularly absorption and scattering coefficients, holds both scientific and economic significance for assessing the climatic impacts arising from regional and long-term aerosol pollution characteristics. In this paper, photoacoustic spectroscopy technology integrating nephelometry technology had been employed for the simultaneous temporal and spatial measurement of aerosol absorption and scattering coefficients. A dedicated gas cell and detection system were constructed, with the design process outlined in detail. Experimental results illustrated that the combined utilization of the resonant photoacoustic cell and integrating sphere enabled the simultaneous detection of aerosol absorption and scattering coefficients. This underscored the potential applicability of the proposed method in analyzing aerosol optical properties, offering a solution for in-situ stereo-monitoring of atmospheric aerosols.
KEYWORDS: Photoacoustic spectroscopy, Signal detection, Thermal effects, Adsorption, Nitrogen dioxide, Photolysis, Signal to noise ratio, Light sources, Temperature metrology, Temperature control
Nitrogen dioxide (NO2) is a toxic gas to organisms, and one of the main factors in forming acid rain. Recently, some scholars have developed NO2 photoacoustic detection setups, but there are few reports about the effects of thermal decomposition and adsorption on photoacoustic detection of NO2. This work carried out detailed research on NO2 photoacoustic detection. Based on the photolysis effect of NO2, the thermal decomposition effect of NO2 excited by high-power laser was found and verified. Additionally, to reduce the influence of the adsorption effect of the photoacoustic cell wall on the detection results, a temperature control model of the photoacoustic cell was constructed, and the optimal detection temperature of 30 ℃ was ensured through experiments. The cross experimental verification was conducted with acetylene (C2H2) that was not decomposed by high temperature, which further explained the influence of NO2 thermal decomposition and adsorption effect on the detection accuracy. Based on the research results, a photoacoustic detection setup was built with a 450 nm laser and a differential H-type photoacoustic cell as the core. The experimental results showed that when the photoacoustic cell temperature was 30 ℃, the minimum detection limit of 206 ppt was achieved within 5 s average detection time. In conclusion, this work provides a reference for developing of high-precision NO2 photoacoustic detection setup.
Hazardous chemicals leakage and explosions issues severely threaten public safety and social development. In recent years, Raman spectroscopy has the advantages of multi-component detection, with a simple device and nondestructive detection, and it has been applied in the detection of hazardous chemicals. There are many advantages of excitation UV compared to visible or near-IR counterparts: 1) Solar blind detection enabling standoff operation in full daylight; 2) Fluorescence-free Raman enabling enhanced detection and identification of target materials without interference; 3) Eye retina safe. Based on the above analysis, a compact proximal UV-Raman spectroscopy setup was built in the laboratory. A 266nm UV laser with a high repetition rate was used as the light source in the setup, which has many advantages, such as a cramped structure with an air-cooled device and low energy. An independently built Galileo transmission telescope was be used to collect signals in the setup. In addition, a customized UV high-sensitivity fiber spectrometer was used to detect the Raman signals. Typical hazardous chemicals (dichloromethane, anhydrous ethanol, potassium nitrate) were detected at 1000mm using the built setup. The experimental results indicated that clear Raman signals of the hazardous chemicals could be detected when the exposure time of the spectrometer was only 15ms (satisfied the conditions of human eyes safety). A new eye-safe UVRaman spectroscopy technology in this paper provides method support for rapidly detecting hazardous chemicals in the future.
This study determined the potential of wavelet-based analysis for extracting spectral features of hyperspectral reflectance signals. The dyadic discrete wavelet transform is proposed for feature extraction from a high dimensional data space. The wavelet's inherent multi-resolution properties are discussed in terms related to multi-spectral and hyperspectral remote sensing. Wavelet can focus on the local structure of the signal through adjusting the scale parameter in the course of focusing. So we can find the singularities and the inflexions of the original signal. The absorption strips are thus detected consequently with the local wavelet transform modulus (absolute value) maxima. The results show a superior performance of the proposed wavelet-based features that are more meaningful for spectral feature extraction when compared to conventional methods.
Fourier transform infrared (FuR) spectrometer with its high radiometric reproducibility wavelength accuracy signal-to-noise ratio and spectral resolution has been established as an ideal tool for air pollutants detection in city. Because most pollutant gases exhibit significant absorptivities in infrared spectral regions FTIR system can measure their hyper-spectral absorption signatures analyze the composition and provide the concentration estimation. The flexibility of airborne FTIR system in combination with its great detectable distance makes it even more suitable for the detection of pollutant cloud. This paper focuses on the detection principle of airborne pollutants and concemed signal processing techniques such as the establishment of spectral database pro-processing and characteristic extraction identification and classification for spectral signals pollution evaluation simulation techniques and etc.
The Tibet Plateau plays an important role in the general atmospheric circulation as a cold and heat source. In summer, the plateau is covered by active cumulus convection. So it is very important to understand the distribution of cloud over Tibet. In this paper, Meteosat image data is used to analyze the cloud cover over Tibet. The statistical method is used to perform cloud classification: first step, a two-dimensional frequency histogram is constructed from two channels images (VIS and IR); second step, dynamic clustering technique is used to automatically classify this two-dimensional histogram; third step, each pixel in original image is assigned to a cloud class. The classification results show clearly the distribution of cloud classes.
In this paper, we make research on the analysis and processing for some data from an in-flight absolutely radiometric calibration experiment of imaging spectrometer. We use two kinds of methods to retrieve the surface reflectance of some targets on the ground and make a comparison with measured results.
KEYWORDS: Wavelets, Remote sensing, Infrared spectroscopy, Data processing, Spectroscopy, Signal processing, Neural networks, Algorithms, Data acquisition, Algorithm development
In this paper we will first introduce the basic theory and algorithms of wavelet techniques, and then will try to use it to process the remote sensing spectral data acquired by IR Fourier transform spectrometer, we also discuss that self- adaptive wavelet network applied to classify the spectra. The experimental results show that wavelet techniques is very efficient in processing and classification of spectral data.
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