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Tailoring thermal stability of ceria-zirconia mixed oxide by doping of rare earth elements:From theory to experiment

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Journal of Rare Earths 2022年第8期40卷 1272-1280页

作者： Zheng Zhao Xiaowei Huang Yongqi Zhang Juanyu Yang Meisheng Cui Yongke Hou Zongyu Feng National Engineering Research Center for Rare Earth Materials GRINM Group Corporation LimitedBeijing 100088China Griem Hi-tech Co. Ltd.Langfang 065000China Rare Earth Functional Materials(Xiong’an)Innovation Center Co. Ltd.Baoding 071700China

Ceria-zirconia mixed oxides(CZMO)are widely used in many important catalysis ***,pure CZMO is known to have poor thermal *** this paper,a strategy was proposed to design Ce_(0.475)Zr_(0.475)M_(0.05)O_(2)(M=La,Y,Pr,Nd,Pm,Sm,Eu,Gd,Tb,Er,Lu,and,Yb)oxide surface with high thermal stability by using first-principles molecular dynamics(FPMD)simulation and experiment *** the structure stability analysis at different temperatures,the surface energyγas a function of R_(ion)/D_(ave)is identified as a quantitative structure descriptor for analyzing the doping effect of rare earth(RE)elements on the thermal stability of Ce_(0.475)Zr_(0.475)M_(0.05)O_(2).By doping the suitable RE,γcan be adjusted to the optimal range to enhance the thermal stability of Ce_(0.475)Zr_(0.475)M_(0.05)O_(2).With this strategy,it can be predicted that the sequence of thermal stability improvement is Y>La>Gd>Nd>Pr>Pm>Sm>Eu>Tb>Er>Yb>Lu,which was further verified by our experiment *** thermal treatment at 1100℃for 10 h,the specific surface area(SSA)of aged Y-CZ and La-CZ samples can reach 21.34 and 19.51 m~2/g,which is 63.02%and 49.04%higher than the CZMO sample without doping because the surface doping of Y and La is in favor of inhibiting the surface atoms thermal *** a word,the strategy proposed in this work can be expected to provide a viable way for designing the highly efficient CZMO materials in extensive applications and promoting the usages of the high-abundance rare-earth elements Y and La.

关键词： Cerium zirconium oxides surface Thermal stability Doping structure descriptor Rare earth metals Theory and experiment

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Structural Characterization and Retention Time Simulation of Allergenic Fragrances

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Chinese Journal of Structural Chemistry 2020年第10期39卷 1753-1762,1741页

作者： LI Jian-Feng LIAO Li-Min College of Chemistry and Chemical Engineering Neijiang Normal UniversityNeijiang 641100China Key Laboratory of Fruit Waste Treatment and Resource Recycling Neijiang 641100China

By classifying non-hydrogen atoms of organic compounds,parametric dyeing,and establishing the relationship between non-hydrogen atoms,new structure descriptors were *** structures of 48 common allergenic fragrance organic compounds were parametrically *** multiple linear regression(MLR)and partial least-squares regression(PLS)methods were used to build two models of relationship between the compound structure and chromatographic retention *** stability of the models was evaluated by the"leave-one-out"cross test,and the predictive ability of the models was tested using an external sample *** correlation coefficients(R2)of the two models are 0.9791 and 0.9744,those(R(CV)~2)of the cross test are 0.8542 and 0.7464,and those(R(test)~2)of the external prediction are 0.9802 and 0.9367,indicating that the models built have good fitting ability,stability and external forecasting *** structural factors affecting the chromatographic retention time of the compounds were *** results show that the compound with more secondary carbon atoms may have larger chromatographic retention time(tR)*** paper has certain reference value for the study on the relationship between the structures and properties of allergenic fragrance organic compounds.

关键词： allergenic fragrance structure descriptor structure characterization simulation prediction

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An Adaptive PM Model Based on Difference Eigenvalue for Image Restoration

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Chinese Journal of Electronics 2014年第4期23卷 761-766页

作者： TIAN Haiying CAI Hongmin LAI Jianhuang School of Information Science and Technology Sun Yat-sen University School of Computer Science and Engineering South China University of Technology

Classic Perona-Malik(PM) model is usually used to smooth noise for the degraded image. However, to our knowledge, a well-known defect of the PM model is prone to cause ‘staircase’ effect and blur image fine feature because of the lack of correct diffusion strength in diffusion process. To tackle the problem, we will improve the PM model by developing a new structure descriptor to adjust anisotropic diffusion strength for smoothing noise and preserving image feature adaptively. The new structure descriptor is based on image local geometry-Hessian matrix, called difference eigenvalue, which can adaptively track edge feature and high degree of homogeneity in an image, even when the observed image is blurred. Experiments on both nature and medical images show that the improved PM method can achieve a superior performance than the traditional methods in terms of visual inspection and quantitative measurements.

关键词： Hessian matrix structure descriptor Anisotropic diffusion Noise reduction

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Recent Implementations in LASP 3.0:Global Neural Network Potential with Multiple Elements and Better Long-Range Description

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Chinese Journal of Chemical Physics 2021年第5期34卷 583-590,I0003页

作者： Pei-lin Kang Cheng Shang Zhi-pan Liu Collaborative Innovation Center of Chemistry for Energy Material Shanghai Key Laboratory of Molecular Catalysis and Innovative MaterialsKey Laboratory of Computational Physical Science(Ministry of Education)Department of ChemistryFudan UniversityShanghai 200433China Shanghai Qi Zhi Institute Shanghai 200030China

LASP(large-scale atomistic simulation with neural network potential)software developed by our group since 2018 is a powerful platform(***)for performing atomic simulation of complex *** software integrates the neural network(NN)potential technique with the global potential energy surface exploration method,and thus can be utilized widely for structure prediction and reaction mechanism *** we introduce our recent update on the LASP program version 3.0,focusing on the new functionalities including the advanced neuralnetwork training based on the multi-network framework,the newly-introduced S^(7) and S^(8) power type structure descriptor(PTSD).These new functionalities are designed to further improve the accuracy of potentials and accelerate the neural network training for multipleelement *** Cu-C-H-O neural network potential and a heterogeneous catalytic model as the example,we show that these new functionalities can accelerate the training of multi-element neural network potential by using the existing single-network potential as the *** obtained double-network potential Cu CHO is robust in simulation and the introduction of S^(7) and S^(8) PTSDs can reduce the root-mean-square errors of energy by a factor of two.

关键词： Large-scale atomistic simulation with neural network potential Machine learning Neural network structure descriptor Simulation software

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