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A New Method to Determine the Energy Loss of Heavy Ions in Solids

  • Horst Schmidt-Böcking
  • Gerd Rühle
  • Klaus Bethge

Abstract

When today’s physicist needs data about the differential energy loss of heavy ions, he refers to the tables by Northcliffe and Schilling (Nuclear Data Tables A7) [1]. However, these data are obtained for heavy ions in a semiempirical way and hardly examined experimentally for ions heavier than oxygen in solids. We have thus developed a method by which quick and reliable energy loss data for heavy ions in solids can be determined. If the elastic scattering cross section as a function of the energy is known, then it is possible to determine a larger energy range of the differential energy loss from one single measurement.

Keywords

Energy Loss Thick Target Surface Barrier Detector Total Energy Loss Virtual Path 
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.

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References

  1. [1]
    L. C. Northcliffe and R. F. Schilling, Nucl. Data Tables A7, 233 (1970).ADSCrossRefGoogle Scholar
  2. [2]
    W. Booth, I. S. Grant, Nucl. Phys. 63, 481 (1965).CrossRefGoogle Scholar
  3. [3]
    D. Ward et al., Progress Report 1971 (Aug.), Chalk River.Google Scholar
  4. [4]
    H. Schmidt-Böcking et al., Jahresbericht 1972, MPI für Kernphysik Heidelberg.Google Scholar
  5. [5]
    P. G. Roll et al., Nucl. Phys. 17, 54 (1960).CrossRefGoogle Scholar
  6. [6]
    P. G. Roll et al., Phys. Rev. 120Nr. 2, 470 (1960).ADSCrossRefGoogle Scholar
  7. [7]
    D. I. Porat et al., Proc. Phys. Soc. (London) 78, 1135 (1961).ADSCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1975

Authors and Affiliations

  • Horst Schmidt-Böcking
    • 1
  • Gerd Rühle
    • 1
  • Klaus Bethge
    • 1
  1. 1.II. Physikal. InstitutUniv. Heidelberg69 HeidelbergGermany

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