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Sökning: WFRF:(Ding J) > Luleå tekniska universitet

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1.
  • Zoback, Mary Lou, et al. (författare)
  • Global patterns of tectonic stress
  • 1989
  • Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 341:6240, s. 291-298
  • Forskningsöversikt (refereegranskat)abstract
    • Regional patterns of present-day tectonic stress can be used to evaluate the forces acting on the lithosphere and to investigate intraplate seismicity. Most intraplate regions are characterized by a compressional stress regime; extension is limited almost entirely to thermally uplifted regions. In several plates the maximum horizontal stress is subparallel to the direction of absolute plate motion, suggesting that the forces driving the plates also dominate the stress distribution in the plate interior.
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2.
  • Larsson, Andreas, et al. (författare)
  • Modelling of Carbon Nanotube Catalytic Growth
  • 2008
  • Konferensbidrag (refereegranskat)abstract
    • Carbon nanotubes (CNTs) have; due to their remarkable mechanical; electronic and thermal properties; many suggested uses; and have even been demonstrated as interconnects and nano-transistors in laboratory built devices [1-4]. The reason CNTs are not yet incorporated into electronics is due to growth control and placement issues. With present day state-of-the-art techniques it is not possible to grow CNTs with only one property (i.e. either all metallic or all semiconducting); which presents the first and principal hurdle for the utilisation of CNTs in semiconductor industry. It is; however; possible to grow CNTs of a certain type (multi-walled; double-walled; or single walled); within a rather narrow diameter distribution. It is also well understood how the orientation of the honey-comb structure relative to the CNT axis determines the property of the CNT itself. The problem lies in realizing growth of CNTs with control over this internal graphene structuring. We have performed first-principles calculations of how single-walled carbon nanotubes (SWNTs) bond with different metal nanoparticles explaining why the traditional catalysts (Fe; Co; Ni) are more successful than other metals (Cu; Pd; Au) [5]; and how this realization relates to new nanocomposite catalyst particles (Cu/Mo) [6]. We will present our contribution to understanding the mechanism of catalytic CNT growth; since it is only through better knowledge that property-controlled growth of CNTs can be achieved
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3.
  • Hedman, Daniel, et al. (författare)
  • Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations
  • 2024
  • Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 15
  • Tidskriftsartikel (refereegranskat)abstract
    • Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great promise for advanced technologies, provided their structure remains uniform throughout their length. Their growth takes place at high temperatures across a tube-catalyst interface. Structural defects formed during growth alter CNT properties. These defects are believed to form and heal at the tube-catalyst interface but an understanding of these mechanisms at the atomic-level is lacking. Here we present DeepCNT-22, a machine learning force field (MLFF) to drive molecular dynamics simulations through which we unveil the mechanisms of CNT formation, from nucleation to growth including defect formation and healing. We find the tube-catalyst interface to be highly dynamic, with large fluctuations in the chiral structure of the CNT-edge. This does not support continuous spiral growth as a general mechanism, instead, at these growth conditions, the growing tube edge exhibits significant configurational entropy. We demonstrate that defects form stochastically at the tube-catalyst interface, but under low growth rates and high temperatures, these heal before becoming incorporated in the tube wall, allowing CNTs to grow defect-free to seemingly unlimited lengths. These insights, not readily available through experiments, demonstrate the remarkable power of MLFF-driven simulations and fill long-standing gaps in our understanding of CNT growth mechanisms.
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