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Interface enhanced superconductivity in monolayer FeSe film on oxide substrate

Ding Cui Liu Chong Zhang Qing-Hua Gong Guan-Ming Wang Heng Liu Xiao-Zhi Meng Fan-Qi Yang Hao-Hao Wu Rui Song Can-Li Li Wei He Ke Ma Xu-Cun Gu Lin Wang Li-Li Xue Qi-Kun

Han Zhong-Ming, Li Sheng-Nan, Zheng Chen-Ye, Duan Da-Gao, Yang Wei-Jie. Link prediction model based on dynamic network representation. Acta Phys. Sin., 2020, 69(16): 168901. doi: 10.7498/aps.69.20191162
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Interface enhanced superconductivity in monolayer FeSe film on oxide substrate

Ding Cui, Liu Chong, Zhang Qing-Hua, Gong Guan-Ming, Wang Heng, Liu Xiao-Zhi, Meng Fan-Qi, Yang Hao-Hao, Wu Rui, Song Can-Li, Li Wei, He Ke, Ma Xu-Cun, Gu Lin, Wang Li-Li, Xue Qi-Kun
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  • We report on the observation of a superconducting gap of about 14-15 meV, significantly enlarged compared with the value of 2.2 meV for bulk FeSe, in monolayer FeSe film interfaced with MgO epitaxial on SrTiO3(001) substrate by using the scanning tunneling microscopy. While the MgO exhibits the same work function as SrTiO3 substrate, the gap magnitude is in coincidence with that of surface K-doped two-unit-cell FeSe film on SrTiO3(001), suggesting that the interface enhanced superconductivity might be attributed to cooperation of interface charge transfer driven by band bending with interface electron-phonon coupling as discovered at FeSe/TiO2 interfaces. On the other hand, the observation of such an enlarged superconducting gap, complementary to our previous transport observation of an onset superconducting transition temperature of 18 K in monolayer FeSe film on a bulk MgO substrate, implies that FeSe/MgO interface is likely to be a new interface high-temperature superconducting system, providing a new platform for investigating the mechanism of interface hightemperature superconductivity.
      Corresponding author: Wang Li-Li, liliwang@mail.tsinghua.edu.cn;qkxue@mail.tsinghua.edu.cn ; Xue Qi-Kun, liliwang@mail.tsinghua.edu.cn;qkxue@mail.tsinghua.edu.cn
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 11574174, 11774193, 11790311, 51522212, 51421002, 51672307), the National Basic Research Program of China (Grant Nos. 2015CB921000, 2014CB921002), and the Strategic Priority Research Program of the Chinese Academy of Sciences, China (Grant No. XDB07030200).
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    期刊类型引用(3)

    1. 钱榕,许建婷,张克君,董宏宇,邢方远. 隐马尔可夫模型的异质网络链接预测方法研究. 通信学报. 2022(05): 214-225 . 百度学术
    2. 李一帆,王玙. 基于动态网络表示学习的学者合作关系预测研究. 情报科学. 2022(06): 115-123 . 百度学术
    3. 莫小梅,沈浩,俞定国. 基于复杂网络的全球新闻流动模式分析. 西南大学学报(自然科学版). 2020(12): 15-24 . 百度学术

    其他类型引用(4)

  • [1]

    Wang Q Y, Li Z, Zhang W H, et al. 2012 Chin. Phys. Lett. 29 037402

    [2]

    Song C L, Wang Y L, Cheng P, et al. 2011 Science 332 1410

    [3]

    Zhang W H, Sun Y, Zhang J S, et al. 2014 Chin. Phys. Lett. 31 017401

    [4]

    Zhang Z, Wang Y H, Song Q, et al. 2015 Sci. Bull. 60 1301

    [5]

    Ge J F, Liu Z L, Liu C, et al. 2015 Nat. Mater. 14 285

    [6]

    Peng R, Xu H C, Tan S Y, et al. 2014 Nat. Commun. 5 5044

    [7]

    Zhou G, Zhang D, Liu C, et al. 2016 Appl. Phys. Lett. 108 202603

    [8]

    Zhang P, Peng X L, Qian T, et al. 2016 Phys. Rev. B 94 104510

    [9]

    Zhang C, Liu Z, Chen Z, et al. 2017 Nat. Commun. 8 14468

    [10]

    Ding H, Lv Y F, Zhao K, et al. 2016 Phys. Rev. Lett. 117 067001

    [11]

    Rebec S N, Jia T, Zhang C, et al. 2017 Phys. Rev. Lett. 118 067002

    [12]

    Wang L, Ma X C, Xue Q K 2016 Supercond. Sci. Technol. 29 123001

    [13]

    Wang L, Xue Q K 2017 AAPPS Bull. 27 4

    [14]

    Maletz J, Zabolotnnyy V B, Evtushinsky D V, et al. 2014 Phys. Rev. B 89 220506(R)

    [15]

    He S, He J, Zhang W, et al. 2013 Nat. Mater. 12 605

    [16]

    Tan S, Zhang Y, Xia M, et al. 2013 Nat. Mater. 12 634

    [17]

    Lee J J, Schmitt F T, Moore R G, et al. 2014 Nature 515 245

    [18]

    Song C L, Zhang H M, Zhong Y, et al. 2016 Phys. Rev. Lett. 116 157001

    [19]

    Guo J, Jin S, Wang G, et al. 2010 Phys. Rev. B 82 180520

    [20]

    Lu X F, Wang N Z, Wu H, et al. 2015 Nat. Mater. 14 325

    [21]

    Shiogai J, Ito Y, Mitsuhashi T, et al. 2015 Nat. Phys. 12 42

    [22]

    Lei B, Cui J H, Xiang Z J, et al. 2016 Phys. Rev. Lett. 116 077002

    [23]

    Hanzawa K, Sato H, Hiramatsu H, et al. 2016 Proc. Natl. Acad. Sci. U.S.A. 113 3986

    [24]

    Zhang S, Guan J, Jia X, et al. 2016 Phys. Rev. B 94 081116

    [25]

    Zhang H, Zhang D, Lu X, et al. 2017 Nat. Commun. 8 214

    [26]

    Tang C, Liu C, Zhou G, et al. 2016 Phys. Rev. B 93 020507

    [27]

    Zhang W H, Liu X, Wen C H, et al. 2016 Nano Lett. 16 1969

    [28]

    Xie Y, Cao H Y, Zhou Y, et al. 2015 Sci. Rep. 5 10011

    [29]

    Wang Y, Linscheid A, Berlijn T, et al. 2016 Phys. Rev. B 93 134513

    [30]

    Li Z X, Wang F, Yao H, et al. 2016 Sci. Bull. 61 925

    [31]

    Lee D H 2018 Ann. Rev. Conden. Matter Phys. 9 261

    [32]

    Chu C W, Deng L Z, Lv B 2015 Physica C 514 290

    [33]

    Kamihara Y, Watanabe T, Hirano M, et al. 2008 J. Am. Chem. Soc. 130 3296

    [34]

    Ren Z, Lu W, Yang J, et al. 2008 Chin. Phys. Lett. 25 2215

    [35]

    Dingle R, Stormer H L, Gossard A C, Wiegmann W 1978 Appl. Phys. Lett. 33 665

    [36]

    Zhou G, Zhang Q, Zheng F, et al. 2018 Sci. Bull. 63 747

    [37]

    Binnig G, Rohrer H 1983 Surf. Sci. 126 236

    [38]

    Lim J Y, Oh J S, Ko B D, et al. 2003 J. Appl. Phys. 94 764

    [39]

    Susaki T, Shigaki N, Matsuzaki K, et al. 2014 Phys. Rev. B 90 035453

    [40]

    Li F, Zhang Q, Tang C, et al. 2016 2D Mater. 3 024002

    [41]

    Choubey P, Berlijn T, Kreisel A, et al. 2014 Phys. Rev. B 90 134520

    [42]

    Liu C, Mao J, Ding H, et al. 2018 Phys. Rev. B 97 024502

    [43]

    Zhang W, Li Z, Li F, et al. 2014 Phys. Rev. B 89 060506

    [44]

    Parlinski K, Łazewski J, Kawazoe Y 2000 J. Phys. Chem. Solids 61 87

    [45]

    Wang Y, Liu Z K, Chen L Q, et al. 2006 J. Appl. Phys. 100 023533

    [46]

    Oshima C, Aizawa T, Souda R, et al. 1990 Solid State Commun. 73 731

    [47]

    Coh S, Lee D H, Louie S G, et al. 2016 Phys. Rev. B 93 245138

    [48]

    Niu F, Meier A L, Wessels B W 2006 J. Vac. Sci. Technol. B 24 2586

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  • 期刊类型引用(3)

    1. 钱榕,许建婷,张克君,董宏宇,邢方远. 隐马尔可夫模型的异质网络链接预测方法研究. 通信学报. 2022(05): 214-225 . 百度学术
    2. 李一帆,王玙. 基于动态网络表示学习的学者合作关系预测研究. 情报科学. 2022(06): 115-123 . 百度学术
    3. 莫小梅,沈浩,俞定国. 基于复杂网络的全球新闻流动模式分析. 西南大学学报(自然科学版). 2020(12): 15-24 . 百度学术

    其他类型引用(4)

Metrics
  • Abstract views:  9912
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  • Cited By: 7
Publishing process
  • Received Date:  10 September 2018
  • Accepted Date:  21 September 2018
  • Published Online:  20 October 2019

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