Engineering self-organising helium bubble lattices in tungsten

被引:32
|
作者
Harrison, R. W. [1 ]
Greaves, G. [1 ]
Hinks, J. A. [1 ]
Donnelly, S. E. [1 ]
机构
[1] Univ Huddersfield, Sch Comp & Engn, Huddersfield HD1 3DH, W Yorkshire, England
来源
SCIENTIFIC REPORTS | 2017年 / 7卷
基金
英国工程与自然科学研究理事会;
关键词
ONE-DIMENSIONAL MIGRATION; VOID-LATTICE; INTERSTITIAL DIFFUSION; TRANSMUTATION ELEMENTS; RADIATION-DAMAGE; MOLYBDENUM; SUPERLATTICE; SIMULATIONS; MECHANISMS; COPPER;
D O I
10.1038/s41598-017-07711-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The self-organisation of void and gas bubbles in solids into superlattices is an intriguing nanoscale phenomenon. Despite the discovery of these lattices 45 years ago, the atomistics behind the ordering mechanisms responsible for the formation of these nanostructures are yet to be fully elucidated. Here we report on the direct observation via transmission electron microscopy of the formation of bubble lattices under He ion bombardment. By careful control of the irradiation conditions, it has been possible to engineer the bubble size and spacing of the superlattice leading to important conclusions about the significance of vacancy supply in determining the physical characteristics of the system. Furthermore, no bubble lattice alignment was observed in the <111> directions pointing to a key driving mechanism for the formation of these ordered nanostructures being the two-dimensional diffusion of selfinterstitial atoms.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] Special Section on Self-organising Networks
    Ott, Jorg
    Passarella, Andrea
    PERVASIVE AND MOBILE COMPUTING, 2011, 7 (01) : 78 - 78
  • [42] Self-Organising Maps for Image Segmentation
    Wehrens, Ron
    ADVANCES IN DATA ANALYSIS, DATA HANDLING AND BUSINESS INTELLIGENCE, 2010, : 373 - 383
  • [43] Self-organising Neural Network Hierarchy
    Borgohain, Satya
    Kowadlo, Gideon
    Rawlinson, David
    Bergmeir, Christoph
    Loo, Kok
    Rangarajan, Harivallabha
    Kuhlmann, Levin
    AI 2020: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 12576 : 359 - 370
  • [44] Model Probability in Self-organising Maps
    Angelopoulou, Anastassia
    Psarrou, Alexandra
    Garcia-Rodriguez, Jose
    Mentzelopoulos, Markos
    Gupta, Gaurav
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II, 2013, 7903 : 1 - +
  • [45] Ising, Schelling and self-organising segregation
    D. Stauffer
    S. Solomon
    The European Physical Journal B, 2007, 57 : 473 - 479
  • [46] Self-organising fuzzy logic classifier
    Gu, Xiaowei
    Angelov, Plamen P.
    INFORMATION SCIENCES, 2018, 447 : 36 - 51
  • [47] SOPRO - Advancements in the Self-organising Production
    Chemnitz, Moritz
    Krueger, Joerg
    Patzlaff, Marcel
    Tuguldur, Erdene-Ochir
    2010 IEEE CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2010,
  • [48] Analytic Comparison of Self-Organising Maps
    Mayer, Rudolf
    Neumayer, Robert
    Baum, Doris
    Rauber, Andreas
    ADVANCES IN SELF-ORGANIZING MAPS, PROCEEDINGS, 2009, 5629 : 182 - +
  • [49] Kernel self-organising maps for classification
    Lau, K. W.
    Yin, H.
    Hubbard, S.
    NEUROCOMPUTING, 2006, 69 (16-18) : 2033 - 2040
  • [50] Biotic analogies for self-organising cities
    Narraway, Claire L.
    Davis, Oliver S. P.
    Lowell, Sally
    Lythgoe, Katrina A.
    Turner, J. Scott
    Marshall, Stephen
    ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE, 2020, 47 (02) : 268 - 286