To guarantee the security of new energy vehicles (NEV), which include energy storage devices such as batteries, a quartz crystal microbalance (QCM) sensor was designed to detect online the HF gas produced from the leakage of electrolyte in the power system. Based on the chemical properties of HF gas, an amino-functionalized metal–organic framework NH2-MIL-101 (Cr) was synthesized as a sensing material of a QCM transducer to detect HF gas for NEV safeguard. The sensing materials are designed based on the hydrogen bond interaction between the amino group and HF molecular and were characterized by powder X-ray diffraction, Brunauer–Emmett–Teller (BET) surface area analysis, Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS) and thermogravimetric analysis (TGA), etc. The performance of this sensor showed high sensitivity, with a limit of detection at 500 ppb, short response/recovery time and good reproducibility for anhydrous hydrogen fluoride (AHF) detection. Additionally, the sensing mechanism of NH2-MIL-101(Cr) QCM resonator to AHF is revealed to be reversible chemical adsorption by Gaussian 09. It is well-matched with a result of experimental determination through temperature-varying microgravimetric experiments. Therefore, the amino-functionalized MIL-101(Cr) QCM resonator may be a good candidate for an NEV safety monitor due to its rapid response to HF leaked from the decomposition of the electrolyte.
Post-processing methods can be used to reduce the biases of hydrological models. In this research, six post-processing methods are compared: quantile mapping (QM) methods, which include four kinds of transformations, and two newly established machine learning frameworks [support vector regression (SVR) and convolutional neural network (CNN)] based on meteorological data and variation mode decomposition (VMD)-decomposed streamflow. These post-processing methods are applied to a distributed model (WRF-Hydro), and the evaluation is carried out over five watersheds with different areas in South China. The post-processing methods are separately applied to calibrated and uncalibrated models. The results show that the SVR- and CNN-based post-processing methods perform better than the QM methods in terms of daily streamflow simulations in different areas with different topographies in the Xijiang River basin. There are large uncertainties in the QM post-processing methods. The CNN-based post-processing performs slightly better than the SVR-based post-processing, but both methods can markedly improve the simulated streamflow. The CNN- and SVR-based post-processing frameworks are suitable for both calibration and test periods. The differences between post-processing with uncalibrated and calibrated models are quite small for SVR- and CNN-based post-processing, but large for QM post-processing. For WRF-Hydro, the CNN- and SVR-based post-processing methods consume much less time and computational resources than model calibration.
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