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学者姓名：葛新
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Abstract ：
The development of green and easily regulated amphiphilic particles is crucial for advancing Pickering emulsion catalysis. In this study, lignin particles modified via sulfobutylation were employed as solid emulsifiers to support Pd nanoparticles (NPs), thereby enhancing the catalytic efficiency of biphasic reactions. Sulfobutylation of lignin effectively adjusted the hydrophilic-hydrophobic balance, resulting in controlled emulsion types and droplet sizes. Pd NPs were loaded onto lignin with a 50% sulfobutylation ratio through the in situ reduction of active functional groups, further stabilized by the cross-linked network structure of lignin. In the hydrogenation of nitrobenzene, the Lig-50S-0.6Pd catalyst exhibited superior activity compared to traditional Pd/C catalysts, which is attributed to the high retention of active sites and increased interfacial area. This work underscores the potential for designing lignin-based amphiphilic particles and developing Pickering emulsion-enhanced reactions.
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| GB/T 7714 | Gao, Xuewei , Hou, Linxi , Yang, Weijun et al. Lignin-Based Nanoparticles Stabilized Pickering Emulsion for Enhanced Catalytic Hydrogenation [J]. | LANGMUIR , 2025 , 41 (3) : 1937-1947 . |
| MLA | Gao, Xuewei et al. "Lignin-Based Nanoparticles Stabilized Pickering Emulsion for Enhanced Catalytic Hydrogenation" . | LANGMUIR 41 . 3 (2025) : 1937-1947 . |
| APA | Gao, Xuewei , Hou, Linxi , Yang, Weijun , Dong, Liangliang , Ge, Xin . Lignin-Based Nanoparticles Stabilized Pickering Emulsion for Enhanced Catalytic Hydrogenation . | LANGMUIR , 2025 , 41 (3) , 1937-1947 . |
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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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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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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 R-2 = 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.
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, Jiaying , Hou, Linxi , Nan, Jing 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, Jiaying 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, Jiaying , Hou, Linxi , Nan, Jing , Ni, Bangqing , Dai, Wei , Ge, Xin . 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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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 ：
Aqueous micellar catalysis has become recognized as an efficient, green and sustainable technology, yet is still challenging to improve the catalytic selectivity by manipulating the microenvironment of bimetallic nanoalloys. In this work, we present a water-based micelle-bimetal strategy to regulate effectively the selective hydrogenation of alpha,(3-unsaturated aldehydes. This strategy employed surfactant GluM to stabilize PtCu bimetallic alloys and enrich the substrate to reinforce the contact with NPs. Importantly, the steric effect created by surfactant GluM can modulate the adsorption mode of alpha,(3-unsaturated aldehydes on the alloy surface from planar configuration to linear configuration. The remarkable conversion (up to 98.5 %) and selectivity (up to 94.6 %) for the hydrogenation of alpha,(3-unsaturated aldehydes to unsaturated alcohols were achieved, attributed to a synergy of favorable factors, such as alloy effect, multifunctionality of GluM and auxiliary effect of H2O. This work not only highlights the important role of the specific microenvironment surrounding the bimetal alloys in regulating the catalytic selectivity, but also provides a novel reference for improving catalytic selectivity in aqueous micelles.
Keyword ：
beta-unsaturated aldehydes beta-unsaturated aldehydes Bimetal alloy Bimetal alloy Selective hydrogenation Selective hydrogenation Surfactant Surfactant Water Water
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| GB/T 7714 | Chen, Yiru , He, Xi , Hou, Linxi et al. Probing the surfactant-constructed microenvironment of PtCu bimetallic nanoalloys to improve catalytic selectivity in aqueous micelles [J]. | CHEMICAL ENGINEERING JOURNAL , 2024 , 500 . |
| MLA | Chen, Yiru et al. "Probing the surfactant-constructed microenvironment of PtCu bimetallic nanoalloys to improve catalytic selectivity in aqueous micelles" . | CHEMICAL ENGINEERING JOURNAL 500 (2024) . |
| APA | Chen, Yiru , He, Xi , Hou, Linxi , Liu, Bing , Dong, Liangliang , Ge, Xin . Probing the surfactant-constructed microenvironment of PtCu bimetallic nanoalloys to improve catalytic selectivity in aqueous micelles . | CHEMICAL ENGINEERING JOURNAL , 2024 , 500 . |
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