Superconductivity and Magnetism in Silicon and Germanium Clathrates

  • Joseph H. RossJr
  • Yang Li


Clathrates are materials containing closed polyhedral cages stacked to form crystalline frameworks. With Si, Ge, and Sn atoms populating these frameworks, a wide variety of electronic and vibrational properties can be produced in these materials, by substitution upon framework sites or through incorporation of ions in cage-center positions. Commonly formed structures include the type I, type II, and chiral clathrate types, whose properties will be described here. Ba8Si46 with the type-I structure has been found to exhibit superconductivity with T c as high as 9 K. The enhanced T c in this compound has been shown to arise predominantly from very sharp features in the electronic densities of states associated with the extended sp 3-bonded framework. Atomic substitution can tailor these electronic properties; however, the associated disorder has been found to inevitably lower the T c due to the disrupted continuity of the framework. Efforts to produce analogous Ge-based superconductors have not been successful, due to the appearance of spontaneous vacancies, which also serve to disrupt the frameworks. The formation of these vacancies is driven by the Zintl mechanism, which plays a much more significant role for the structural stability of the Ge clathrates. The sharp density of states features in these extended framework materials may also lead to enhanced magnetic features, due to conduction electron-mediated coupling of substituted magnetic ions. This has led to magnetic ordering in Fe- and Mn-substituted clathrates. The largest number of clathrates exhibiting magnetic behavior has been produced by substitution of Eu on cage-center sites, with a ferromagnetic T c as high as 38 K observed in such materials.


Methane Hydrate Alkali Metal Atom Early Transition Metal Late Transition Metal Framework Atom 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported in part by the Robert A. Welch Foundation (Grant No. A-1526) and the NSF (Grant No. DMR-0821284).


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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of PhysicsTexas A&M UniversityCollege StationUSA
  2. 2.Department of Engineering Science and MaterialsUniversity of Puerto Rico at MayaguezMayaguezUSA

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