Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network.

Ammonia (NH3) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH3 is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical absorption spectroscopy (UV-DOAS) and spectral reconstruction fitting neural network (SRFNN) for detecting NH3 in EB. UV-DOAS is introduced to eliminate interference from slow change absorption in the EB spectrum while spectral reconstruction fitting is proposed for the first time to map the original spectra onto the sine function spectra by the principle of least absolute deviations. The sine function spectra are then fitted by the least-squares method to eliminate noise signals and the interference of exhaled nitric oxide. Finally, the neural network is built to enable the detection of NH3 in EB at parts per billion (ppb) level. The laboratory results show that the detection range is 9.50-12425.82 ppb, the mean absolute percentage error (MAPE) is 0.83%, and the detection accuracy is 0.42%. Experimental results prove that the sensor can detect breath NH3 and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH3 with high accuracy and stability in the medical field.

Measurement of the absorption cross-sections of sulfur compounds in the 180-270 nm region considering nonlinear effects.

Sulfur compounds (SO2, CS2, H2S and OCS) are common toxic pollutants in the atmospheric environment, and the absorption spectroscopy technique can indeed help to realize online monitoring of their concentrations. However, nonlinear effects that occur during absorption spectroscopy measurements have a serious impact on the measurement of the absorption cross-sections (ACSs) of sulfur compounds, leading to serious deviations in both the substance absorption properties and concentrations obtained based on ACS analysis. In this paper, the maximum effective ACSs of sulfur compounds in the linear region are obtained by considering the influence of nonlinear effects and eliminating interference factors such as oxygen and photolysis. In addition, the nonlinear effects are found to be greatly attenuated in spectra with broad band absorption characteristics by comparing the oscillatory absorption spectra before and after the differential treatment and by comparing the change in the oscillatory ACS with the broad band ACS. The experimental results show that the effective ACSs of SO2, CS2, H2S, and OCS with a resolution of 0.23 nm are 14.15 × 10-18 cm2 per molecule, 5.61 × 10-16 cm2 per molecule, 7.09 × 10-18 cm2 per molecule, and 3.20 × 10-19 cm2 per molecule, respectively. So far, it is the largest ACS obtained at room temperature and atmospheric pressure, which is of great significance for online measurement of sulfur compounds.

Quantitative detection of multicomponent SF6 decomposition products based on Fourier transform infrared spectroscopy combined with SCARS-DNN.

Accurate and efficient quantitative analysis of the decomposition products of the insulating medium SF6 in gas-insulated switchgear (GIS) is important for an effective assessment of its internal insulation status. In this work, a quantitative calibration model of Fourier Transform Infrared Spectroscopy (FTIR) combined with SCARS-DNN (Stability Competitive Adaptive Reweighted Sampling-Deep Neural Network) is proposed for the rapid non-destructive detection of SF6 decomposition products. First, the interference of the background gas SF6 on the absorption spectra of the decomposition products is eliminated according to the Lambert-Beer law, while baseline correction and Savitzky-Golay (S-G) smoothing are used to remove baseline drift and noise. Subsequently, a Monte Carlo cross-validation method is used to detect and eliminate the anomalous samples. Then feature selection is performed using uninformative variable elimination (UVE) and stability competitive adaptive reweighted sampling (SCARS), and finally quantitative calibration models of FULL-DNN (full spectral band), UVE-DNN, and SCARS-DNN are developed. For the quantitative detection of SF6 decomposition products, the SCARS-DNN model had the best prediction performance with a maximum reduction of 96.18% in the root mean square error (RMSE) and 96.11% in the mean absolute percentage error (MAPE). Results reveal that the relative errors are basically kept below 1.36% when predicting the three decomposition products, even in the presence of a high level of SF6 interference. Therefore, the SCARS-DNN model is suitable for high-precision quantitative detection of SF6 decomposition gas.

Exploring the interplay of new energy vehicle enterprises, consumers, and government in the context of the "dual carbon" target: An evolutionary game and simulation analysis.

To achieve low-carbon and green mobility, the government needs to encourage people to buy and use new energy vehicles. This study proposes a tripartite evolutionary game model among new energy vehicle manufacturers, consumers, and government agencies. The game strategy combinations of each party and the stability conditions of the equilibrium point in the evolutionary game system are analyzed, and the validity of the conclusions is verified by simulation results. Compared with traditional studies that suggest the government should adopt the direct subsidy policy, this study shows that in the early stage of new energy vehicle development, government subsidies are still important for the rapid growth of new energy vehicle production and sales, but indirect policies can play a key role as the new energy vehicle industry matures. In addition to the price, the attractiveness of vehicle brands, the perceived utility of the products among consumers, and the coverage of charging infrastructure in cities also determine whether consumers decide to purchase and use new energy vehicles. The findings could provide useful recommendations for governments and manufacturers of new energy vehicles to meet their "dual carbon" targets.