Fine tuning the structure of bimetallic nanoparticles is critical toward understanding structure−activity relationships and further improving the catalytic performance in propane dehydrogenation (PDH). Excessive Fe species in the PtFe bimetallic catalysts promote carbon deposition leading to low propylene selectivity, and it remains challenging to synthesize welldefined PtFe catalysts while selectively eliminating the excessive Fe. Herein, we show that the formation of coke can be significantly inhibited by introducing CO 2 into the PDH over PtFe catalysts, where CO 2 effectively eliminates the active Fe(0) coking sites without changing the catalytic surface structure of the PtFe alloy. With a CO 2 /C 3 H 8 feeding ratio of 0.20, the Pt1Fe7/S-1 catalyst shows the highest propylene production rate and decreased amount of coke from 18.8 to 1.0 wt % compared with dehydrogenation without CO 2 . X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and 57 Fe Mossbauer results indicate that it is the oxidation of excessive unalloyed Fe species during the CO 2 -PDH reaction, instead of the reverse Boudouard reaction (CO 2 + C = 2CO), that significantly inhibits the carbon deposition. This work provides a promising strategy for tuning the structure of PtFe bimetallic catalysts under reaction conditions and improving the performance of the PDH reaction.

Density-based spatial clustering of applications with noise (DBSCAN) is a typical kind of algorithm based on density clustering in unsupervised learning. It can cluster data of arbitrary shape and also identify noise samples in the dataset. However, an unavoidable defect of the DBSCAN algorithm exists since the clustering performance is quite sensitive to the parameter settings of MinPts and Eps, and there is no theory to guide the setting of its parameters. Therefore, a new method is proposed to optimize the DBSCAN parameters in this paper. Multi-verse optimizer algorithm, a special variable updating method with excellent optimization performance, is selected and improved for optimizing the parameters of DBSCAN, which not only can quickly find out the highest clustering accuracy of DBSCAN, but also find the interval of Eps corresponding to the highest accuracy. In order to search the range of Eps more quickly and efficiently, we design a new mechanism for the variable update of MVO. The experimental results show that the improved MVO is used to optimize DBSCAN, which not only can quickly find out its highest clustering accuracy but also can search the parameters of MinPts and Eps corresponding to the highest clustering accuracy efficiently. INDEX TERMS Improved MVO, DBSCAN, parameter optimization, unsupervised learning.

A series of Fe-substituted ZSM-5 zeolite samples with an almost constant Si/(Fe + Al) molar ratio of 50 but varied levels of Fe substitution were synthesized via an in situ seed-induced hydrothermal method in a fluoride medium. Additionally, a hierarchical Fe-substituted sample with high diffusion capability was also produced by a subsequent alkaline treatment. The textural and acidic properties of these samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV−Raman, Fourier transform infrared (FT-IR), UV−vis, H 2 -temperature programmed reduction (TPR), Ar adsorption−desorption, NH 3 -temperature-programmed desorption (TPD), X-ray fluorescence (XRF), 27 Al, and 29 Si magicangle spinning (MAS) NMR analysis. These analyses revealed that Fe 3+ species were effectively integrated into the MFI framework of the Fe-substituted samples. The as-synthesized samples displayed orthogonally intergrown crystal plates of increased aspect ratios with increased Fe-substitutions. More significantly, the resulted Fe-substituted samples displayed a noticeably reduced acid strength compared to the pure ZSM-5. Evaluated by the benzene alkylation reaction with dilute ethylene, the 50% partially Fe-substituted ZSM-5 sample showed a combined high ethyl selectivity and long catalytic lifetime among all catalysts studied, which is attributed to its suitably balanced acid strength.

Under the slow varying ambient electric field, positive leader propagation exhibits steps characterized by intense reilluminations and abrupt elongations. These steps are currently not well understood. In this work, we investigate these steps in laboratory atmospheric discharges, using a high‐speed video camera and a synchronized electrical parameter measurement system. The discharge, emitting weak light and preceding the intense reillumination, is discovered. This finding suggests that the leader channel actually restarts and extends forward before the intense reillumination, which deepens our understanding of the dynamic process of the positive leader step. The discharge before the intense reillumination contributes to the corona inception from the electrode, leading to the intense reillumination of the leader channel and the emergence of an intense corona streamer burst from the leader tip.

Accurate identification of coal and gangue is an important prerequisite for the effective separation of coal and gangue. The application of imaging technology combined with image processing steps (like enhancement, feature extraction, etc.) and classifier is used to identify coal and gangue, which effectively avoids the shortcomings of traditional methods (radiation, pollution, etc.). However, ordinary image detection is greatly influenced by environmental factors such as light, dust and so on. Multispectral imaging technology, as a new generation of optical non-destructive testing technology, is less affected by illumination, so we propose a new solution for the recognition of coal and gangue by using multispectral imaging. Firstly, we respectively tested the classification performance of different image feature extraction methods under GS-SVM, GA-SVM, and PSO-SVM classifiers, and selected the best feature extraction method is LBP. And then, we compared the classification effects under different wavelengths and found that the ninth wavelength works best. That is, the difference in imaging between coal and gangue at 773.776 nm is greatest. Finally, the performance of the proposed model for the identification of coal and gangue was carried out. And the highest classification accuracy can be obtained by using GS-SVM as the classifier, at which point, C = 8, g = 0.17678. The results show that multispectral imaging technology can be used for the identification of coal and gangue, and the prediction accuracy of the model combined with LBP feature extraction and GS-SVM can reach 96.25% (77/80). The conclusions could provide reference evidence for the intelligent dry selection in coal preparation plants and underground coal mine. INDEX TERMS Coal-gangue identification, multispectral imaging, feature extraction, support vector machine.