The selective oxidation of aromatics to synthesize valuedadded products is of great significance for chemical industry. Molecule oxygen as oxidant is the priority for this process, however, low efficiency was usually obtained. In this work, a new strategy for aromatics oxidation using O 2 and Tert-butyl hydroperoxide (TBHP) as dual oxidants without catalyst was proposed. Typical radical process was observed when Toluene was used as the substrate. As high as 47.1 % conversion and 87.6 % selectivity of benzol acid were obtained, while a negligible or low activity were observed under O 2 or TBHP oxidative systems. Detailed investigation shows that the distribution of products was almost the same between TBHP + N 2 and TBHP + O 2 processes, in which the very close active energies were obtained (46.0 and 46.3 kJ/mol). These results indicated that TBHP + N 2 or TBHP + O 2 involved reactions underwent the same reaction process. The mechanism study demonstrated that the decomposition of TBHP could produce abundance of t-BuO * and * OH radicals, which significantly enhanced the αH abstraction from toluene and formed alkyl radicals. The alkyl radicals then react with O 2 to produce peroxides, which started the radical propagation cycle. The overall rate would be enhanced subsequently. Furthermore, an extension application of other aromatic substrates was investigated and displayed a great promising future to produce aromatic products.
The catalytic synthesis of lactones from alkanes is a novel strategy from the viewpoint of environmentally benign technology. In this work, we propose a new protocol of using a typical alkane (cyclohexane, CyH) as a substrate to produce lactone (ε-caprolactone, ε-CL) in the presence of carbon nanotubes (CNTs) and aldehydes. The results demonstrated that CNTs could be used as an efficient catalyst for the production of ε-CL. Nitrogen doping would greatly enhance the activity, with 26.2% conversion of CyH and 87% selectivity of ε-CL. The specific N dopants served as complex active sites for improving the catalytic efficiency of NCNTs with an optimum content of N (3.38 atom %). Further investigations revealed that the ratio of pyridine N/ quaternary N probably played a significant role in the facilitation of this reaction, which displayed a linear relationship with the specific activity of NCNTs. A mechanistic study demonstrated that the activation of benzaldehyde for the production of carbonyl radicals was considered as the initial reaction, which activated CyH molecules to produce alkyl radicals, thus promoting the oxidation of CyH. The intermediate product CyO was found to be the main precursor of ε-CL, and during the reaction, cyclohexanol (Cy−OH) would likely be transformed into CyO.
The establishment and development of an offshore RMB financial market has far-reaching implications. As an important component of China’s offshore financial market, Hong Kong’s RMB offshore financial market is an objective prerequisite for China’s long-term financial liberalization and the establishment of a new “double-loop” development model. Although Hong Kong’s offshore RMB financial market has made great progress in the past decade, there are still some shortcomings. This paper analyzes the development trend of Hong Kong’s offshore RMB from the perspective of the characteristics and advantages of Hong Kong’s RMB offshore market, and identifies the problems, and then discusses the development direction and focus of Hong Kong’s offshore RMB.
The limitation of the small-scale expression samples generally causes the performance degradation for facial expression recognition-based methods. Also, the correlation between different expression is always ignored when performing feature extraction process. Given above, we propose a novel approach that develops multi-class differentiation feature representation guided joint dictionary learning for FER. The proposed approach mainly includes two steps: firstly, we construct multi-class differentiation feature dictionaries corresponding to different expressions of training samples, aiming to enlarge inter-expression distance to mitigate the problem of nonlinear distribution in training samples. Secondly, we joint learn the multiple feature dictionaries by optimizing the resolutions of each feature dictionary, aiming to establish the strong relationship and enhance the representation ability among multiple feature dictionaries. To sum up, the proposed approach has more discriminative ability from the representation perspective. Comprehensive experiments carried out using three public datasets, including JAFFE, CK+, and KDEF datasets, demonstrate that the proposed approach has strong performance for small-scale samples compared to several state-of-the-art methods.
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