Currently, wearable sensors have been widely used for human behavior monitoring. Among them, triboelectric nanogenerator (TENG) textile has the functions of power generation and active sensing, showing great advantages in energy collection and signal transmission. However, the TENG textiles face problems such as limited recognition accuracy, insufficient flexibility, missed fashion sense, and complex manufacturing processes. To address the above issues, a core–shell structure integrated conductive fiber (C-fiber) based on ionogel and a hollow silicone tube are displayed. Owing to the fact that the C-fiber can be synthesized through a simple one-step UV curing process, it has the potential for large-scale production. Due to the dyeability of ionogel and the transparency of hollow tube, the C-fiber has the ability to obtain personalized colors. This fiber can be further woven into a flexible fabric sensor (W-TENG) for high-precision human behavior monitoring. Noteworthiness, the W-TENG still has flexibility and output signal in environments ranging from −18 to 200 °C,the strong wide temperature adaptability stem from the heat and frost resistance of the materials. By assembling W-TENG onto the human body, daily movements, fall types, and post fall states can be perceived. Furthermore, complex motion data is trained by support vector machine (SVM), and the recognition accuracy for different fall categories can reach 100%. In practical applications, TENG could be equipped with custom bluetooth modules to monitor falls in real-time during walking, and provide timely treatment for the injured.
Food safety has been an issue of common concern in today’s society. Excessive benzoic acid (BA) additives in wheat flour would pose a huge threat to human health. In this paper, terahertz (THz) time-domain spectroscopy was used to detect BA quantitatively, which is a preservative additive in flour. Samples were prepared by grinding, drying, weighing, mixing and performing successively according to the designed concentration gradient (concentration 0.04%, 0.08%, 0.1%, 0.2%, 0.4%, 0.5%, 1%, 1.5%, …, 10%), with the BA concentration ranging from 0.040% to 19.99%. Then the THz Spectra of wheat flour samples with different concentrations of BA were collected. After preprocessing the original terahertz spectra with multiple scattering correction and other methods, Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), Principal Component Analysis (PCA) and Uninformative Variable Elimination (UVE) were used to select effective spectral bands, and then Partial Least Squares (PLS) and Least Square Support Vector Machine (LS-SVM) models were established respectively. It turned out that the absorption peak of BA was at 1.94 THz of terahertz absorption coefficient, which increased with the rise of BA concentration. The results also indicated that better modeling results could be obtained by using CARS method for variable selection. And the established CARS-PCA-LS-SVM model had the best prediction result by combining PCA with CARS, with the correlation coefficient of the prediction (R p ) of 0.9956 and Root Mean Square Error of Prediction (RMSEP) of 0.64%. Hence, it can be concluded that terahertz technology combined with LS-SVM model can well achieve the quantitative detection of BA concentration in flour. Although the model is not universal since samples size is not large enough, the research method provides a basis for the detection of BA additives in flour.
危险品化学品经营许可证（带存储）