基于遗传算法的Au-Cu-Pt三元合金纳米粒子的稳定结构研究

李铁军; 孙跃; 郑骥文; 邵桂芳; 刘暾东

doi:10.7498/aps.64.153601

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摘要

合金纳米粒子展示出单金属粒子所不具有的多功能性能, 而其稳定结构的研究对于进一步了解其催化性能具有重要的意义. 本文采用改进的遗传算法和量子修正Sutton-Chen型多体势对二十四面体Au-Cu-Pt三元合金纳米粒子的稳态结构进行了系统的研究. 针对不同尺寸、不同组成比例的合金纳米粒子, 探讨了遗传算法的收敛性及初始构型对稳态结构的影响. 计算的结果表明: 初始结构的选取并不影响最终的稳定结构, 并且改进的遗传算法具有较好的稳定性; Au和Cu形成表面偏聚, 而Pt则倾向于分布在内层; 当Au或Cu比例较小时, Au和Cu表现出表面最大偏聚; 当Au与Cu原子数之和大于表面原子数时, 二者表现出竞争偏聚, 且Cu的偏聚效应较强; 随着Au, Cu原子数继续增长至大于表面和次表面原子数之和时, Au的偏聚性能增强. 此外, Cu在占据表面后, 会越过次外层, 与Pt在内层形成混合相结构.

关键词:

作者及机构信息

1.
重庆大学自动化学院, 重庆 400044;

2.
集美大学信息工程学院, 厦门 361021;

3.
厦门大学自动化系, 厦门 361005

基金项目: 国家自然科学基金(批准号: 51271156, 61403318)、福建省自然科学基金(批准号: 2013J06002, 2013J01255)和中央高校基本科研业务费(批准号: 2012121010)资助的课题.

参考文献

[1]	Zhou Z Y, Tian N, Li J T, Broadwell I, Sun S G 2011 Chem. Soc. Rev. 40 4167
[2]	Ferrando R, Jellinek J, Johnston R L 2008 Chem. Rev. 108 845
[3]	Balerna A, Evangelisti C, Schiavi E, Vitulli G, Bertinetti L, Martra G, Mobilio S 2013 J. Phys.: Conf. Ser. 430 012052
[4]	Yun K, Cho Y H, Cha P R, Lee J, Nam H S 2012 Acta Mater 60 4908
[5]	Huang R, Shao G F, Wen Y H, Sun S G 2014 Phys. Chem. Chem. Phys. 16 22754
[6]	Deng Y J, Tian N, Zhou Z Y, Huang R, Liu Z L, Xiao J, Sun S G 2012 Chem. Sci. 3 1157
[7]	Cheng D J, Liu X, Cao D P 2007 Nanotechnology 18 475702
[8]	Kahanal S, Nabraj B, Velazquez-Salazar JJ 2013 Nanoscale 5 12456
[9]	Bhagiyalakshmi M, Anuradha R, ParBull S D 2010 Bull. Korean Chem. Soc. 31 120
[10]	Kang S W, Lee Y W, Park Y S 2013 ACS Nano 7 7945
[11]	Fan T E, Liu T D, Zheng J W, Shao G F, Wen Y H 2015 J. Mater. Sci. 50 3308
[12]	Guo S J, Zhang S, Sun X L, Sun S H 2011 J. Am. Chem. Soc. 133 15354
[13]	Tian N, Zhou Z Y, Sun S G, Ding Y, Wang Z L 2007 Science 316 732
[14]	Sun X L, Li D G, Ding Y, Zhu W L, Guo S J, Wang Z L, Sun S H 2014 J. Am. Chem. Soc. 136 5745
[15]	Liu T D,Zheng J W, Shao G F, Fan T E, Wen Y H 2015 Chin. Phys. B 24 033601
[16]	Oh J S, Nam H S, Choi J H, Lee S C 2013 Met. Mater. Int. 19 513
[17]	Lv J, Wang Y, Zhu L, Ma Y 2012 J. Chem. Phys. 137 084104
[18]	Chen Z, Jiang X, Li J, Li S, Wang L 2013 J. Comput. Chem. 34 1046
[19]	Chen Z H, Jiang X W, Li J B, Li S S 2013 J. Chem. Phys. 138 214303
[20]	Liu T D, Chen J R, Hong W P, Shao G F, Wang T N, Zheng J W, Wen Y H 2013 Acta Phys. Sin. 62 193601
[21]	Cagin T, Kimura Y, Qi Y, Li H, Ikeda H, Johnson W L, Goddard W A 1999 Mater. Res. Soc. Symp. Proc. 554 43
[22]	Li S F, Zhao X J, Xu X S, Gao Y F, Zhang Z Y 2013 Phys. Rev. Lett. 111 115501
[23]	Zhang H J, Watanabe T, Okumura M, Haruta M, Toshima N 2012 Nature Mater. 11 49
[24]	Liu T D, Fan T E, Shao G F, Zheng J W, Wen Y H 2014 Phys. Lett. A 378 2965
[25]	Xiao S, Hua W, Luo W, Wu Y, Li X, Deng H 2006 Eur. Phys. J. B 54 479

施引文献

[1]	Zhou Z Y, Tian N, Li J T, Broadwell I, Sun S G 2011 Chem. Soc. Rev. 40 4167
[2]	Ferrando R, Jellinek J, Johnston R L 2008 Chem. Rev. 108 845
[3]	Balerna A, Evangelisti C, Schiavi E, Vitulli G, Bertinetti L, Martra G, Mobilio S 2013 J. Phys.: Conf. Ser. 430 012052
[4]	Yun K, Cho Y H, Cha P R, Lee J, Nam H S 2012 Acta Mater 60 4908
[5]	Huang R, Shao G F, Wen Y H, Sun S G 2014 Phys. Chem. Chem. Phys. 16 22754
[6]	Deng Y J, Tian N, Zhou Z Y, Huang R, Liu Z L, Xiao J, Sun S G 2012 Chem. Sci. 3 1157
[7]	Cheng D J, Liu X, Cao D P 2007 Nanotechnology 18 475702
[8]	Kahanal S, Nabraj B, Velazquez-Salazar JJ 2013 Nanoscale 5 12456
[9]	Bhagiyalakshmi M, Anuradha R, ParBull S D 2010 Bull. Korean Chem. Soc. 31 120
[10]	Kang S W, Lee Y W, Park Y S 2013 ACS Nano 7 7945
[11]	Fan T E, Liu T D, Zheng J W, Shao G F, Wen Y H 2015 J. Mater. Sci. 50 3308
[12]	Guo S J, Zhang S, Sun X L, Sun S H 2011 J. Am. Chem. Soc. 133 15354
[13]	Tian N, Zhou Z Y, Sun S G, Ding Y, Wang Z L 2007 Science 316 732
[14]	Sun X L, Li D G, Ding Y, Zhu W L, Guo S J, Wang Z L, Sun S H 2014 J. Am. Chem. Soc. 136 5745
[15]	Liu T D,Zheng J W, Shao G F, Fan T E, Wen Y H 2015 Chin. Phys. B 24 033601
[16]	Oh J S, Nam H S, Choi J H, Lee S C 2013 Met. Mater. Int. 19 513
[17]	Lv J, Wang Y, Zhu L, Ma Y 2012 J. Chem. Phys. 137 084104
[18]	Chen Z, Jiang X, Li J, Li S, Wang L 2013 J. Comput. Chem. 34 1046
[19]	Chen Z H, Jiang X W, Li J B, Li S S 2013 J. Chem. Phys. 138 214303
[20]	Liu T D, Chen J R, Hong W P, Shao G F, Wang T N, Zheng J W, Wen Y H 2013 Acta Phys. Sin. 62 193601
[21]	Cagin T, Kimura Y, Qi Y, Li H, Ikeda H, Johnson W L, Goddard W A 1999 Mater. Res. Soc. Symp. Proc. 554 43
[22]	Li S F, Zhao X J, Xu X S, Gao Y F, Zhang Z Y 2013 Phys. Rev. Lett. 111 115501
[23]	Zhang H J, Watanabe T, Okumura M, Haruta M, Toshima N 2012 Nature Mater. 11 49
[24]	Liu T D, Fan T E, Shao G F, Zheng J W, Wen Y H 2014 Phys. Lett. A 378 2965
[25]	Xiao S, Hua W, Luo W, Wu Y, Li X, Deng H 2006 Eur. Phys. J. B 54 479

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基于遗传算法的Au-Cu-Pt三元合金纳米粒子的稳定结构研究

1. 重庆大学自动化学院, 重庆 400044;

2. 集美大学信息工程学院, 厦门 361021;

3. 厦门大学自动化系, 厦门 361005

基金项目:

国家自然科学基金(批准号: 51271156, 61403318)、福建省自然科学基金(批准号: 2013J06002, 2013J01255)和中央高校基本科研业务费(批准号: 2012121010)资助的课题.

收稿日期: 2014-12-18

修回日期: 2015-04-06

刊出日期: 2015-08-05

摘要: 合金纳米粒子展示出单金属粒子所不具有的多功能性能, 而其稳定结构的研究对于进一步了解其催化性能具有重要的意义. 本文采用改进的遗传算法和量子修正Sutton-Chen型多体势对二十四面体Au-Cu-Pt三元合金纳米粒子的稳态结构进行了系统的研究. 针对不同尺寸、不同组成比例的合金纳米粒子, 探讨了遗传算法的收敛性及初始构型对稳态结构的影响. 计算的结果表明: 初始结构的选取并不影响最终的稳定结构, 并且改进的遗传算法具有较好的稳定性; Au和Cu形成表面偏聚, 而Pt则倾向于分布在内层; 当Au或Cu比例较小时, Au和Cu表现出表面最大偏聚; 当Au与Cu原子数之和大于表面原子数时, 二者表现出竞争偏聚, 且Cu的偏聚效应较强; 随着Au, Cu原子数继续增长至大于表面和次表面原子数之和时, Au的偏聚性能增强. 此外, Cu在占据表面后, 会越过次外层, 与Pt在内层形成混合相结构.

关键词:

Stable structure optimization of Au-Cu-Pt trimetallic nanoparticles based on genetic algorithm

1.
School of Automation, Chongqing University, Chongqing 400040, China;

2.
Information Engineering College, Jimei University, Xiamen 361021, China;

3.
Department of Automation, Xiamen University, Xiamen 361005, China

Fund Project:

Project supported by the National Natural Science Foundation of China (Grant Nos. 51271156 61403318), the Natural Science Foundation of Fujian Province of China (Grant Nos. 2013J01255, 2013J0602) and the Fundamental Research Funds for the Central Universities of China (Grant No. 2012121010)

Received Date: 18 December 2014

Accepted Date: 06 April 2015

Published Online: 05 August 2015

Abstract: Alloy nanoparticles exhibit multifunctional properties different from monometallic nanoparticles. Especially, when a third metal is introduced into bimetallic nanoparticles system to form trimetallic nanoparticles, their chemical activities will be further improved. As the catalytic reaction of nanoparticles usually takes place on surfaces, and the activity and stability are closely related to their structures, therefore the research on the stable structure is crucial for understanding their catalytic activities. In addition, the electrochemically synthesized tetrahexahedral nanoparticles bound with highindex facets may exhibit greatly enhanced catalytic activity because of their large density of low coordination sites at the surface. Based on the above reasons, this paper carries out the investigation on the stable structures of tetrahexahedral Au-Cu-Pt trimetallic nanoparticles by using an improved genetic algorithm and the quantum-corrected Sutton-Chen (Q-SC) type many-body potentials. To avoid the genetic algorithm being trapped into premature convergence, two improvement strategies are developed. On the one hand, an atom coordinate ranking operation, which is implemented according to the atomic distance from the core, is proposed for reducing the probability of individual loss. On the other hand, an alternating bit means is introduced into the crossover operation to keep the atomic composition ratio unchanged. Moreover, the performance of genetic algorithm and the influence of original configuration on the stable structures of Au- Cu-Pt trimetallic nanoparticles with different sizes and different compositions also have been investigated. One stochastic distribution structure and three core-shell distribution structures of Au@CuPt, Cu@AuPt and Pt@AuCu are adopted as the initial structures, respectively. Eleven optimization trials on Au-Cu-Pt trimetallic nanoparticles in Au-Cu-Pt system with Au : Cu : Pt of 0:343 : 0:343 : 0:314 with 443 atoms are used to verify that the different original structures should have no effect on the final stable structure. Furthermore, 30 random trails on Au-Cu-Pt trimetallic nanoparticles at Au : Cu : Pt of 0:316 : 0:316 : 0:368 with 443 atoms are conducted to prove that the genetic algorithm can obtain robust results with small standard deviation. Finally, the segregation analysis results show that: In Au-Cu-Pt trimetallic nanoparticles, Au and Cu atoms prefer to aggregate on the surface while Pt atoms are preferential to locate in the core. Furthermore, Cu atoms exhibit stronger surface segregation than Au atoms. For small Au or Cu concentration, Au and Cu atoms would display the maximum segregation. They begin to compete during aggregation, and the Cu atoms have a strong tendency for surface segregation when the number of Au and Cu atoms is bigger than the total number of surface atoms. With increasing number of Au and Cu atoms over those on the surface and sub-surface, Au atoms would display a strong surface segregation than Cu atoms. Additionally, Cu atoms will mix with Pt atoms in the inner layers over the sub-surface after occupying the surface. The distribution of surface atoms has been further examined by the analyses of coordination number: the Cu atoms tend to occupy the vertices, edges and kinks, while the Au atoms preferentially segregate to the flattened surface. This study provides a perspective on structural features and segregation behavior of trimetallic nanoparticles.

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