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学者姓名：葛新
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Abstract ：
Despite the wide utility of micellar palladium (Pd) nanoparticle (NP)-catalyzed Mizoroki-Heck reactions in laboratory and industrial synthesis, the easy construction of micellar Pd NPs and the detailed role of surfactants remain the focus of attention. Here, we present a simple and sustainable strategy to construct a nanoreactor by connecting micelles with Pd NPs. This strategy enables us to obtain ultrasmall Pd NPs with an average particle size of 1.8 nm, mainly in situ synthesized by triethylamine (Et3N) reduction and stabilized by chelating with sugar-based surfactant micelles. The first-order kinetic model related to the initial concentration of Pd-based catalyst is established, and the apparent activation energy of this reaction in aqueous micellar solutions is calculated to be 40.49 kJ mol-1. The mechanism of the active species ultrasmall Pd(0) NPs obtained by the reductive elimination of Pd(ii) precursors by base was demonstrated. Notably, the recycled aqueous reaction mixture containing the micelles and Pd NPs can be reused. A simple and sustainable strategy is proposed to construct a nanoreactor by connecting micelles with in-situ prepared ultrasmall Pd NPs to efficiently catalyze the Mizoroki-Heck reaction.
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| GB/T 7714 | Luo, Xiaojun , Wu, Siyuan , Hou, Linxi et al. Ligand-free ultrasmall palladium nanoparticle catalysis for the Mizoroki-Heck reaction in aqueous micelles [J]. | NEW JOURNAL OF CHEMISTRY , 2024 , 48 (16) : 7102-7110 . |
| MLA | Luo, Xiaojun et al. "Ligand-free ultrasmall palladium nanoparticle catalysis for the Mizoroki-Heck reaction in aqueous micelles" . | NEW JOURNAL OF CHEMISTRY 48 . 16 (2024) : 7102-7110 . |
| APA | Luo, Xiaojun , Wu, Siyuan , Hou, Linxi , Ge, Xin . Ligand-free ultrasmall palladium nanoparticle catalysis for the Mizoroki-Heck reaction in aqueous micelles . | NEW JOURNAL OF CHEMISTRY , 2024 , 48 (16) , 7102-7110 . |
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Abstract ：
Measuring the critical micelle concentration (CMC) of surfactants holds significant importance in comprehending their interfacial properties. However, traditional methods suffer from issues such as lengthy testing durations, low experimental accuracy, and the complexity of theoretical calculations. Herein, a method for predicting CMC is developed by using machine learning (ML) based on the structural differentiation of surfactants. A quantitative structure-property relationship (QSPR) model that can automatically classify and identify surfactants based on differences in their head groups, was established by collecting a diverse CMC dataset of 779 surfactants. Each surfactant molecule is quantitatively chemically described using molecular descriptors to train 5 different ML models by using linear regression and tree-based algorithms. By evaluating model accuracy, the model was established by automatically selecting light gradient boosting machine (LGBM) and gradient boosting decision tree (GBDT) as the optimal algorithms for ionic and nonionic surfactants, respectively. The overall prediction accuracy of the model achieved R2 = 0.944. Our model significantly outperforms the graph convolutional neural network (GCN) model by comparing prediction accuracy on the same surfactant data. Besides, principal component analysis (PCA) highlighted disparities in feature distribution among different types of surfactants, illustrating the model's accuracy and stability based on structural variability and molecular descriptors. This work not only provides valuable insights into the relationship between surfactant molecular structure and CMC but also advances future surfactant design and screening. © 2024 Elsevier B.V.
Keyword ：
Critical micelle concentration Critical micelle concentration Machine learning Machine learning Quantitative structure-property relationship Quantitative structure-property relationship Surfactant Surfactant
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| GB/T 7714 | Chen, J. , Hou, L. , Nan, J. et al. Prediction of critical micelle concentration (CMC) of surfactants based on structural differentiation using machine learning [J]. | Colloids and Surfaces A: Physicochemical and Engineering Aspects , 2024 , 703 . |
| MLA | Chen, J. et al. "Prediction of critical micelle concentration (CMC) of surfactants based on structural differentiation using machine learning" . | Colloids and Surfaces A: Physicochemical and Engineering Aspects 703 (2024) . |
| APA | Chen, J. , Hou, L. , Nan, J. , Ni, B. , Dai, W. , Ge, X. . Prediction of critical micelle concentration (CMC) of surfactants based on structural differentiation using machine learning . | Colloids and Surfaces A: Physicochemical and Engineering Aspects , 2024 , 703 . |
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Abstract ：
Copper -based metal -organic frameworks (Cu-MOFs) are a promising multiphase catalyst for catalyzing C -S coupling reactions by virtue of their diverse structures and functions. However, the unpleasant odor and instability of the organosulfur, as well as the mass -transfer resistance that exists in multiphase catalysis, have often limited the catalytic application of Cu-MOFs in C -S coupling reactions. In this paper, a Cu-MOFs catalyst modi fied by cetyltrimethylammonium bromide (CTAB) was designed to enhance mass transfer by increasing the adsorption of organic substrates using the long alkanes of CTAB. Concurrently, elemental sulfur was used to replace organosulfur to achieve a highly ef ficient and atomeconomical multicomponent C -S coupling reaction. (c) 2024 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.
Keyword ：
Adsorption Adsorption Copper-based metal-organic frameworks Copper-based metal-organic frameworks C-S coupling reaction C-S coupling reaction (Cu-MOFs) (Cu-MOFs) Design Design Multiphase reaction Multiphase reaction
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| GB/T 7714 | Chen, Lixin , Zhang, Hui , Hou, Linxi et al. Metal-organic-framework-derived copper-based catalyst for multicomponent C-S coupling reaction [J]. | CHINESE JOURNAL OF CHEMICAL ENGINEERING , 2024 , 70 : 1-8 . |
| MLA | Chen, Lixin et al. "Metal-organic-framework-derived copper-based catalyst for multicomponent C-S coupling reaction" . | CHINESE JOURNAL OF CHEMICAL ENGINEERING 70 (2024) : 1-8 . |
| APA | Chen, Lixin , Zhang, Hui , Hou, Linxi , Ge, Xin . Metal-organic-framework-derived copper-based catalyst for multicomponent C-S coupling reaction . | CHINESE JOURNAL OF CHEMICAL ENGINEERING , 2024 , 70 , 1-8 . |
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