搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

引用本文:
Citation:

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

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

Li Tie-Jun, Sun Yue, Zheng Ji-Wen, Shao Gui-Fang, Liu Tun-Dong
PDF
导出引用
  • 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.
    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.
    • 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.
    • Funds: 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)
    [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

  • [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

  • [1] 刘暾东, 李泽鹏, 季清爽, 邵桂芳, 范天娥, 文玉华. 基于改进Basin-Hopping Monte Carlo算法的Fen-Ptm(5 n+m 24)合金团簇结构优化. 物理学报, 2017, 66(5): 053601. doi: 10.7498/aps.66.053601
    [2] 邵桂芳, 郑文馨, 涂娜娜, 刘暾东, 文玉华. 高指数晶面Au-Pd纳米合金粒子的稳定结构研究. 物理学报, 2015, 64(1): 013602. doi: 10.7498/aps.64.013602
    [3] 常红伟, 马华, 张介秋, 张志远, 徐卓, 王甲富, 屈绍波. 基于加权实数编码遗传算法的超材料优化设计. 物理学报, 2014, 63(8): 087804. doi: 10.7498/aps.63.087804
    [4] 李鹏飞, 张艳革, 雷雪玲, 潘必才. 锗团簇Ge65, Ge70, Ge75的稳定结构及其电子结构性质. 物理学报, 2013, 62(14): 143602. doi: 10.7498/aps.62.143602
    [5] 刘暾东, 陈俊仁, 洪武鹏, 邵桂芳, 王婷娜, 郑骥文, 文玉华. 基于粒子群算法的Pt-Pd合金纳米粒子的稳定结构研究. 物理学报, 2013, 62(19): 193601. doi: 10.7498/aps.62.193601
    [6] 何然, 黄思训, 周晨腾, 姜祝辉. 遗传算法结合正则化方法反演海洋大气波导. 物理学报, 2012, 61(4): 049201. doi: 10.7498/aps.61.049201
    [7] 俎云霄, 周杰. 基于组合混沌遗传算法的认知无线电资源分配. 物理学报, 2011, 60(7): 079501. doi: 10.7498/aps.60.079501
    [8] 胡晓琴, 谢国锋. 遗传算法优化BaTiO3壳模型势参数. 物理学报, 2011, 60(1): 013401. doi: 10.7498/aps.60.013401
    [9] 汪剑波, 卢俊. 双屏频率选择表面结构的遗传算法优化. 物理学报, 2011, 60(5): 057304. doi: 10.7498/aps.60.057304
    [10] 宋丹, 张晓林. 基于不动点理论的多系统兼容接收机频点选择问题的研究与遗传算法实现. 物理学报, 2010, 59(9): 6697-6705. doi: 10.7498/aps.59.6697
    [11] 鄂箫亮, 段海明. 利用Gupta势结合遗传算法研究ConCu55-n(n=0—55)混合团簇的结构演化及基态能量. 物理学报, 2010, 59(8): 5672-5680. doi: 10.7498/aps.59.5672
    [12] 赵知劲, 徐世宇, 郑仕链, 杨小牛. 基于二进制粒子群算法的认知无线电决策引擎. 物理学报, 2009, 58(7): 5118-5125. doi: 10.7498/aps.58.5118
    [13] 程兴华, 唐龙谷, 陈志涛, 龚 敏, 于彤军, 张国义, 石瑞英. GaMnN材料红外光谱中洛伦兹振子模型的遗传算法研究. 物理学报, 2008, 57(9): 5875-5880. doi: 10.7498/aps.57.5875
    [14] 牛培峰, 张 君, 关新平. 基于遗传算法的统一混沌系统比例-积分-微分神经网络解耦控制研究. 物理学报, 2007, 56(5): 2493-2497. doi: 10.7498/aps.56.2493
    [15] 林 海, 吴晨旭. 基于遗传算法的重复囚徒困境博弈策略在复杂网络中的演化. 物理学报, 2007, 56(8): 4313-4318. doi: 10.7498/aps.56.4313
    [16] 龚春娟, 胡雄伟. 遗传算法优化设计三角晶格光子晶体. 物理学报, 2007, 56(2): 927-932. doi: 10.7498/aps.56.927
    [17] 钟会林, 吴福根, 姚立宁. 遗传算法在二维声子晶体带隙优化中的应用. 物理学报, 2006, 55(1): 275-280. doi: 10.7498/aps.55.275
    [18] 王东风. 基于遗传算法的统一混沌系统比例-积分-微分控制. 物理学报, 2005, 54(4): 1495-1499. doi: 10.7498/aps.54.1495
    [19] 保文星, 朱长纯, 崔万照. 基于克隆选择的混合遗传算法在碳纳米管结构优化中的研究. 物理学报, 2005, 54(11): 5281-5287. doi: 10.7498/aps.54.5281
    [20] 吴忠强, 奥顿, 刘坤. 基于遗传算法的混沌系统模糊控制. 物理学报, 2004, 53(1): 21-24. doi: 10.7498/aps.53.21
计量
  • 文章访问数:  4986
  • PDF下载量:  215
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-12-18
  • 修回日期:  2015-04-06
  • 刊出日期:  2015-08-05

/

返回文章
返回