RESUMEN
Due to numerous edible oil safety problems in China, an automatic oil quality detection technique is urgently needed. In this study, rough set theory and Fourier transform spectrum are combined for proposing a digital identification method for edible oil. First, the Fourier transform spectra of three different types of edible oil samples, including colza oil, waste oil, and peanut oil, are measured. After the input spectra are differentially and smoothly processed, the characteristic wavelength bands are selected with neighborhood rough set attribution reduction (NRSAR). Moreover, the classification models are established based on random forest (RF) and extreme learning machine (ELM) algorithms. Finally, confusion matrix, classification accuracy, sensitivity, specificity, and the distribution of judgment are calculated for evaluating the classification performances of different models and determining the optimal oil identification model. The results show that by using the third-order difference pre-processing method, 193 wavelength bands in the visible range can be reduced to 10 characteristic wavelengths, with a compression ratio of over 88.61%. Using the established NRS-RF and NRS-ELM models, the total identification accuracies are 91.67% and 93.33%, respectively. In particular, the identification accuracy of peanut oil using the NRS-ELM model reaches up to 100%, whereas the identification accuracies obtained using the principal component analysis (PCA)-based models that are commonly used in information processing (PCA-RF and PCA-ELM) are 81.67% and 90.00%, respectively. As compared with feature extraction methods, the proposed NRSAR shows directive advantages in terms of precision, sensitivity, specificity, and the distribution of judgment. In addition, the execution time is also reduced by approximately 1/3. Conclusively, the NRSAR method and NRS-ELM the model in the spectral identification of edible oil show favorable performance. They are expected to bring forth insightful oil identification techniques.
RESUMEN
In this work, we report the remarkable catalytic effects of a novel Ti3C2 MXene-based catalyst (Ni@Ti-MX), which was prepared via self-assembling of Ni nanoparticles onto the surface of exfoliated Ti3C2 nanosheets. The resultant Ni@Ti-MX catalyst, characterized by ultradispersed Ni nanoparticles being anchored on the monolayer Ti3C2 flakes, was introduced into MgH2 through ball milling. In situ transmission electron microscopy (TEM) analysis revealed that a synergetic catalytic effect of multiphase components (Mg2Ni, TiO2, metallic Ti, etc.) derived in the MgH2 + Ni@Ti-MX composite exhibits remarkable improvements in the hydrogen sorption kinetics of MgH2. In particular, the MgH2 + Ni@Ti-MX composite can absorb 5.4 wt % H2 in 25 s at 125 °C and release 5.2 wt % H2 in 15 min at 250 °C. Interestingly, it can uptake 4 wt % H2 in 5 h even at room temperature. Furthermore, the dehydrogenation peak temperature of the MgH2 + Ni@Ti-MX composite is about 221 °C, which is 50 and 122 °C lower than that of MgH2 + Ti-MX and MgH2, respectively. The excellent hydrogen sorption properties of the MgH2 + Ni@Ti-MX composite are primarily attributed to the peculiar core-shell nanostructured MgH2@Mg2NiH4 hybrid materials and the interfacial coupling effects from different catalyst-matrix interfaces. The results obtained in this study demonstrate that using self-assembling of transition-metal elements on two-dimensional (2D) materials as a catalyst is a promising approach to enhance the hydrogen storage properties of MgH2.