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Aldol condensations of carbonyl compounds for C-C bond formation are a very important class of reactions in organic synthesis and upgrading of biomass-derived feedstocks. However, the atomic level understanding of reaction mechanisms and structure-activity correlation on widely used transition metal oxide catalysts are limited due to the high degree of structural heterogeneity of catalysts such as commercial TiO2 powders. Here, we provide a deep understanding of the reaction mechanisms, kinetics, and structure-function relationships for vapor phase acetone aldol condensation through the controlled synthesis of two catalysts with high surface areas and clean, dominant facets, coupled with detailed characterization and kinetic studies that are further assisted by density functional theory (DFT) calculations. Temperature-dependent diffuse reflectance infrared Fourier transform spectroscopy showed the existence of abundant acetone bonded to surface hydroxyl groups (acetone-OsH) and acetone bonded to Lewis acid sites (acetone-Ti5c) on the surface of both {101} and {001} facet dominant TiO2. Intermolecular C-C coupling of theenolate intermediate from acetone-Ti5c and a vicinal acetone-OsH is a kinetically relevant step, which is consistent with kinetic and isotopic studies as well as DFT calculations. The {001} facet showed a lower apparent activation energy (or higher activity) than the {101} facet. This is likely caused by the weaker Lewis acid and Brønsted base strengths of the {001} facet which favors the reprotonation-desorption of the coupled intermediate, making the C-C coupling step more exothermic on the {001} facet and resulting in an earlier transition state with a lower activation barrier. It is also possible that the {001} facet has a smoother surface configuration and less steric hindrance during intermolecular C-C bond formation than the {101} facet.

This paper aims to evaluate the performance of the integrated composite perforation system in tight reservoirs. To simulate complex pressure conditions of the reservoir, a perforation test device was developed in accordance with API RP 19B sections 4.2 and 4.4 procedures, and a set of test evaluation methods was designed. Using a type 4-1/2in perforating charge and pre-propellant charge, a tight sandstone target with rock physical characteristics similar to the real reservoir were selected for the perforation test under simulated reservoir pressure, pore pressure, and wellbore pressure. The characterization of the perforation tunnel and fracture morphology using CT scanning and thin-section analysis techniques. The results show that under the simulated reservoir pressure conditions, the integrated composite perforation system reduces the penetration depth by nearly 8% compared with the conventional perforation, improves the average flow efficiency by 17.2%, and creates multiple 40mm-100mm extended fractures at the end of the tunnel, the perforation and fracture enhancement is improved by an average of 1.34 times.

With the popularity of the Internet and the increase in the level of information technology, cyber attacks have become an increasingly serious problem. They pose a great threat to the security of individuals, enterprises, and the state. This has made network intrusion detection technology critically important. In this paper, a malicious traffic detection model is constructed based on a decision tree classifier of entropy and a proximal policy optimisation algorithm (PPO) of deep reinforcement learning. Firstly, the decision tree idea in machine learning is used to make a preliminary classification judgement on the dataset based on the information entropy. The importance score of each feature in the classification work is calculated and the features with lower contributions are removed. Then, it is handed over to the PPO algorithm model for detection. An entropy regularity term is introduced in the process of the PPO algorithm update. Finally, the deep reinforcement learning algorithm is used to continuously train and update the parameters during the detection process, and finally, the detection model with higher accuracy is obtained. Experiments show that the binary classification accuracy of the malicious traffic detection model based on the deep reinforcement learning PPO algorithm can reach 99.17% under the CIC-IDS2017 dataset used in this paper. [ABSTRACT FROM AUTHOR]