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学者姓名：孙少瑞
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Abstract ：
Poly(ethylene terephthalate) (PET) is a prevalent single-use plastic, posing a significant threat to the environment and human health due to its nondegradable properties. To confront this pressing issue, there is an urgent need for a method of PET degradation that is not only efficient but also cost-effective. Recognizing the intricate interactions of parameters, we propose an innovative approach that integrates two-step machine learning (ML) techniques to achieve our objective while minimizing experimental costs. A dataset comprising 120 distinct PET degradation conditions was created using high-throughput experimentation (HTE). Initially, eight ML algorithms were employed to train and predict performance, with the decision tree model yielding the most favorable outcomes. Subsequently, the trained ML model was expanded to encompass an extensive array of hypothetical reaction conditions, facilitating the identification of degradation formulations that exhibit exceptional performance. Additionally, through the integration of feature importance analysis, we systematically reconstruct a relevant chemical space. Following regression training and prediction, we identified reaction conditions with significantly higher degradation rates at ambient temperature. The utilization of these conditions not only enhances the efficacy of research and development, leading to reduced experimental costs, but also provides valuable insights for future investigations into PET degradation. In contrast to conventional, resource-intensive trial-and-error approaches, our established platform facilitates the assessment of PET degradation rates across diverse reaction conditions, enabling a preliminary screening for process optimization. Consequently, this approach contributes to the mitigation of solid waste pollution and the advancement of sustainable economic development.
Keyword ：
machine learning machine learning poly(ethylene terephthalate) (PET) poly(ethylene terephthalate) (PET) degradation degradation high-throughputexperimentation high-throughputexperimentation
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| GB/T 7714 | Wang, Yaxin , Li, Shuyuan , Meng, Kong et al. Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation [J]. | ACS SUSTAINABLE CHEMISTRY & ENGINEERING , 2024 , 12 (14) : 5415-5426 . |
| MLA | Wang, Yaxin et al. "Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation" . | ACS SUSTAINABLE CHEMISTRY & ENGINEERING 12 . 14 (2024) : 5415-5426 . |
| APA | Wang, Yaxin , Li, Shuyuan , Meng, Kong , Zhang, Yunjiang , Fang, Zhaolin , Sun, Shaorui . Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation . | ACS SUSTAINABLE CHEMISTRY & ENGINEERING , 2024 , 12 (14) , 5415-5426 . |
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Abstract ：
The recycling of discarded polyethylene terephthalate (PET) plastics produced metal-organic frameworks can effectively minimize environmental pollution and promote sustainable economic development. In this study, we developed a method using NaOH in alcohol and ether solvent environments to degrade PET plastics for synthesizing terephthalic acid. The method achieved a 97.5% degradation rate of PET plastics under a reaction temperature of 80 degrees C for 60 min. We used terephthalic acid as a ligand from the degradation products to successfully synthesize two types of monometallic and bimetallic CoZn-MOF materials. We investigated the impact of different metal centers and solvents on the electrochemical performance of the MOF materials. The result showed that the MOF-DMF/H2O material maintained a specific capacity of 1485.5 mAh g(-1) after 100 cycles at a current density of 500 mA g(-1), demonstrating excellent rate capability and cycling stability. In addition, our finding showed that the performance difference might be attributed to the synergistic effect of bimetallic Co2+ and Zn2+ in MOF-DMF/H2O, rapid lithium-ion diffusion and electron transfer rates, and the absence of coordinating solvents. Additionally, the non-in situ X-ray powder diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy analysis results showed that lithium storage in the MOF-DMF/H2O electrode mainly depended on the aromatic C6 ring and carboxylate portions of the organic ligands in different charge and discharge states. Lithium ions can be reversibly inserted/removed into/from the electrode material. The physical adsorption on the MOF surface through electrostatic interactions enhanced both capacity and cycling stability. This research provides valuable insight for mitigating solid waste pollution, promoting sustainable economic development, and advancing the extensive applications of MOF materials in lithium-ion batteries.
Keyword ：
lithium-ion battery lithium-ion battery lithium storage lithium storage metal-organicframeworks metal-organicframeworks anode materials anode materials polyethyleneterephthalate polyethyleneterephthalate
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| GB/T 7714 | Wang, Yaxin , Meng, Kong , Wang, Huimin et al. Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications [J]. | ACS APPLIED MATERIALS & INTERFACES , 2023 , 16 (1) : 819-832 . |
| MLA | Wang, Yaxin et al. "Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications" . | ACS APPLIED MATERIALS & INTERFACES 16 . 1 (2023) : 819-832 . |
| APA | Wang, Yaxin , Meng, Kong , Wang, Huimin , Si, Yongheng , Bai, Kun , Sun, Shaorui . Green Synthesis of CoZn-Based Metal-Organic Framework (CoZn-MOF) from Waste Polyethylene Terephthalate Plastic As a High-Performance Anode for Lithium-Ion Battery Applications . | ACS APPLIED MATERIALS & INTERFACES , 2023 , 16 (1) , 819-832 . |
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Abstract ：
The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.
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| GB/T 7714 | Li, Shuyuan , Zhang, Yunjiang , Fang, Zhaolin et al. Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 . |
| MLA | Li, Shuyuan et al. "Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING (2023) . |
| APA | Li, Shuyuan , Zhang, Yunjiang , Fang, Zhaolin , Meng, Kong , Tian, Rui , He, Hong et al. Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 . |
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Abstract ：
Recently, in the field of crystal property prediction, the graph neural network (GNN) model has made rapid progress. The GNN model can effectively capture high-dimensional crystal features from the crystal structure, thereby achieving optimal performance in property prediction. However, the existing GNN model faces limitations in handling the hidden layer after the pooling layer, which restricts the training performance of the model. In the present research, we propose a novel GNN model called the batch normalization multilayer perceptron crystal distance graph neural network (BNM-CDGNN). BNM-CDGNN encodes the crystal's geometry structure only based on the distance vector between atoms. The graph convolutional layer utilizes the radial basis function as the attention mask, ensuring the crystal's rotation invariance and adding the geometric information on the crystal. Subsequently, the average pooling layer is connected after the convolutional layer to enhance the model's ability to learn precise information. BNM-CDGNN connects multiple hidden layers after the average pooling layers, and these layers are processed by the batch normalization layer. Finally, the fully connected layer maps the results to the target property. BNM-CDGNN significantly enhances the accuracy of crystal property prediction compared with previous baseline models such as SchNet, MPNN, CGCNN, MEGNet, and GATGNN.
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| GB/T 7714 | Meng, Kong , Huang, Chenyu , Wang, Yaxin et al. BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction [J]. | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 63 (19) : 6043-6052 . |
| MLA | Meng, Kong et al. "BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction" . | JOURNAL OF CHEMICAL INFORMATION AND MODELING 63 . 19 (2023) : 6043-6052 . |
| APA | Meng, Kong , Huang, Chenyu , Wang, Yaxin , Zhang, Yunjiang , Li, Shuyuan , Fang, Zhaolin et al. BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction . | JOURNAL OF CHEMICAL INFORMATION AND MODELING , 2023 , 63 (19) , 6043-6052 . |
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Abstract ：
Cu-based alloy catalysts are widely used in the field of carbon dioxide reduction reaction (CO2RR), due to the good selectivity and low overpotential. In order to achieve efficient exploration of alloy catalysts for CO2RR, a machine learning (ML) model, based on a gradient boosting regression (GBR) algorithm, is developed. By implementing a rigorous feature selection process, the dimensionality of feature space is reduced from thirteen to five, including work function (W), local electronegativity (Loc_EN), electronegativity (EN), interplanar spacing (D), and atomic number (Z), which is referred to as the WLEDZ model. The few-feature model has a high performance as that with many features, and the ML model successfully and rapidly predicts the adsorption energy of the key intermediates (HCOO, CO, and COOH) in the CO2RR process. In addition, eight Cu-based bimetallic catalysts are predicted with highly promising alternatives. This demonstrates that the WLEDZ few-feature ML model can screen highly promising bimetallic alloy for CO2RR and can also be used for the design of other types of catalysts.
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| GB/T 7714 | Xing, Miaojuan , Zhang, Yunjiang , Li, Shuyuan et al. Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ [J]. | JOURNAL OF PHYSICAL CHEMISTRY C , 2022 . |
| MLA | Xing, Miaojuan et al. "Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ" . | JOURNAL OF PHYSICAL CHEMISTRY C (2022) . |
| APA | Xing, Miaojuan , Zhang, Yunjiang , Li, Shuyuan , He, Hong , Sun, Shaorui . Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ . | JOURNAL OF PHYSICAL CHEMISTRY C , 2022 . |
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Abstract ：
Recycling waste PET plastics into metal-organic frameworks is conducive to both pollution alleviation and sustainable economic development. Herein, we have utilized waste PET plastic to synthesize CoNi-MOF applied to lithium battery anode materials via a low-temperature solvothermal method for the first time. The preparation process is effortless, and the sources' conversion rate can reach almost 100%. In addition, the anode performance of MOFs with various Co/Ni mole ratios was investigated. The as-synthesized Co0.8Ni-MOF exhibits excellent crystallinity, purity, and electrochemical performance. The initial discharge and charge capacities are 2496 and 1729 mAh g(-1), respectively. Even after 200 cycles, the Co0.8Ni-MOF electrode can exhibit a high Coulombic efficiency of over 99%. Consequently, given the environmental and economic benefits, the Co0.8Ni-MOF derived from waste PET plastic is thought to be an appealing anode material for lithium-ion batteries.
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| GB/T 7714 | Wang, Yaxin , Wang, Huimin , Li, Shuyuan et al. Waste PET Plastic-Derived CoNi-Based Metal-Organic Framework as an Anode for Lithium-Ion Batteries [J]. | ACS OMEGA , 2022 . |
| MLA | Wang, Yaxin et al. "Waste PET Plastic-Derived CoNi-Based Metal-Organic Framework as an Anode for Lithium-Ion Batteries" . | ACS OMEGA (2022) . |
| APA | Wang, Yaxin , Wang, Huimin , Li, Shuyuan , Sun, Shaorui . Waste PET Plastic-Derived CoNi-Based Metal-Organic Framework as an Anode for Lithium-Ion Batteries . | ACS OMEGA , 2022 . |
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Abstract ：
本发明公布等离子溅射法快速合成一种单原子催化剂的方法及其应用，催化剂载体和活性金属均由等离子溅射方法合成，合成的Pt1/TiOx单原子催化剂催化性能优异。以玻碳片、钛网、不锈钢片、碳纸等作为基板，以载体靶材和活性金属靶材为原料；将基板置于载物台，采用三靶材等离子溅射仪将载体和活性金属交替溅射沉积在基板上，交替沉积若干次即可得到催化剂样品。本发明具有活性金属负载量可控、合成速度快、工艺简单、能耗低等优点，例子中制备的催化剂在过电位50mv时，质量比活性和转换效率(TOF)分别是20％商业铂炭的11.05倍和3.5倍。
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| GB/T 7714 | 孙少瑞 , 田业星 , 王慧敏 et al. 等离子溅射法快速合成一种单原子催化剂的方法及其应用 : CN202110018820.3[P]. | 2021-01-07 . |
| MLA | 孙少瑞 et al. "等离子溅射法快速合成一种单原子催化剂的方法及其应用" : CN202110018820.3. | 2021-01-07 . |
| APA | 孙少瑞 , 田业星 , 王慧敏 , 王亚鑫 . 等离子溅射法快速合成一种单原子催化剂的方法及其应用 : CN202110018820.3. | 2021-01-07 . |
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Abstract ：
Platinum-based materials are identified as the most effective electrocatalysts for the hydrogen evolution reaction (HER) in acidic media. However, the scarcity and high cost of platinum (Pt) have limited its wide application. Therefore, catalysts with low loading but high intrinsic activity can promote the wide application of Pt. In this work, we creatively applied plasma sputtering to deposit Pt single atoms on TiO2-X as an efficient hydrogen evolution reaction (HER) catalyst. The catalyst shows high intrinsic activity and superior stability in acid media, with high turnover frequency (38.9H(2) s(-1)) and mass-specific activity (39.1 A/mg Pt) at 50 mV (vs. RHE) overpotential in 0.5 M H2SO4 that are respectively 3.3 and 11.04 times greater than those of the commercial 20% Pt/C catalyst. In particular, the plasma sputtering method used in this work can shorten the synthesis period of the catalyst to 'minutes' or even 'seconds.' This work provides guidance and inspiration for the fast synthesis of catalysts with high intrinsic activity for various applications. (C) 2021 Elsevier Ltd. All rights reserved.
Keyword ：
Electrocatalytic hydrogen evolution Electrocatalytic hydrogen evolution Noble metal catalyst Noble metal catalyst Turnover frequency Turnover frequency Electrocatalysis Electrocatalysis
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| GB/T 7714 | Tian, Yexing , Yu, Lele , Zhuang, Chunqiang et al. Fast synthesis of Pt single-atom catalyst with high intrinsic activity for hydrogen evolution reaction by plasma sputtering [J]. | MATERIALS TODAY ENERGY , 2021 , 22 . |
| MLA | Tian, Yexing et al. "Fast synthesis of Pt single-atom catalyst with high intrinsic activity for hydrogen evolution reaction by plasma sputtering" . | MATERIALS TODAY ENERGY 22 (2021) . |
| APA | Tian, Yexing , Yu, Lele , Zhuang, Chunqiang , Zhang, Guizhen , Sun, Shaorui . Fast synthesis of Pt single-atom catalyst with high intrinsic activity for hydrogen evolution reaction by plasma sputtering . | MATERIALS TODAY ENERGY , 2021 , 22 . |
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Abstract ：
The low intrinsic activity of Fe/N/C oxygen catalysts restricts their commercial application in the fuel cells technique; herein, we demonstrated the interface engineering of plasmonic induced Fe/N/C-F catalyst with primarily enhanced oxygen reduction performance for fuel cells applications. The strong interaction between F and Fe-N-4 active sites modifies the catalyst interfacial properties as revealed by X-ray absorption structure spectrum and density functional theory calculations, which changes the electronic structure of Fe-N active site resulting from more atoms around the active site participating in the reaction as well as super-hydrophobicity from C-F covalent bond. The hybrid contribution from active sites and carbon support is proposed to optimize the three-phase microenvironment efficiently in the catalysis electrode, thereby facilitating efficient oxygen reduction performance. High catalytic performance for oxygen reduction and fuel cells practical application catalyzed by Fe/N/C-F catalyst is thus verified, which offers a novel catalyst system for fuel cells technique.
Keyword ：
Fe/N/C catalyst Fe/N/C catalyst proton exchange membrane fuel cells proton exchange membrane fuel cells interface engineering interface engineering three-phase microenvironment three-phase microenvironment CF4 plasma treatment CF4 plasma treatment
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| GB/T 7714 | Yin, Xue , Feng, Ligang , Yang, Wen et al. Interface engineering of plasmonic induced Fe/N/C-F catalyst with enhanced oxygen catalysis performance for fuel cells application [J]. | NANO RESEARCH , 2021 , 15 (3) : 2138-2146 . |
| MLA | Yin, Xue et al. "Interface engineering of plasmonic induced Fe/N/C-F catalyst with enhanced oxygen catalysis performance for fuel cells application" . | NANO RESEARCH 15 . 3 (2021) : 2138-2146 . |
| APA | Yin, Xue , Feng, Ligang , Yang, Wen , Zhang, Yuanxi , Wu, Haiyan , Yang, Le et al. Interface engineering of plasmonic induced Fe/N/C-F catalyst with enhanced oxygen catalysis performance for fuel cells application . | NANO RESEARCH , 2021 , 15 (3) , 2138-2146 . |
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Abstract ：
Engineering the local coordination environment in atomically dispersed catalyst has proven to be a prospective route to boost catalyst performance for hydrogen evolution reaction (HER). Herein, we present the utilization of local lattice distortion of TiO2 support to regulate the local coordination environment and electronic structures of atomically dispersed Pt catalysts, resulting in the enhanced performance toward HER. Spectral analysis uncovers an elongated Pt–O bond length, lower Pt–O coordination numbers, as well as less electron holes residing in Pt 5d orbitals for Pt species on distorted TiO2. Density functional theory (DFT) calculation reveal that the variation might weaken the hydrogen adsorption on Pt sites and cause the optimized ΔG value of H∗. As a result, the atomically dispersed Pt catalyst displays superior HER mass activity (62.34 A mg−1 Pt) and the highest turnover frequency (TOF) (56.1H2·s−1) at the 50 mV overpotential in an acid media that are 18.7 and 5.56 times higher than commercial Pt/C. The work should create a new avenue in manipulating the local coordination environment of catalysts via the lattice distortion of the support, and in pursuing desired catalytic performance. © 2021 Elsevier Ltd
Keyword ：
Coordination reactions Coordination reactions Catalyst supports Catalyst supports Electronic structure Electronic structure Density functional theory Density functional theory Bond length Bond length Hydrogen evolution reaction Hydrogen evolution reaction Platinum Platinum Gas adsorption Gas adsorption Spectrum analysis Spectrum analysis Oxide minerals Oxide minerals Hydrogen Hydrogen Titanium dioxide Titanium dioxide
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| GB/T 7714 | Cheng, Xing , Lu, Yue , Zheng, Lirong et al. Engineering local coordination environment of atomically dispersed platinum catalyst via lattice distortion of support for efficient hydrogen evolution reaction [J]. | Materials Today Energy , 2021 , 20 . |
| MLA | Cheng, Xing et al. "Engineering local coordination environment of atomically dispersed platinum catalyst via lattice distortion of support for efficient hydrogen evolution reaction" . | Materials Today Energy 20 (2021) . |
| APA | Cheng, Xing , Lu, Yue , Zheng, Lirong , Pupucevski, Max , Li, Hongyi , Chen, Ge et al. Engineering local coordination environment of atomically dispersed platinum catalyst via lattice distortion of support for efficient hydrogen evolution reaction . | Materials Today Energy , 2021 , 20 . |
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