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Materials and Techniques for Pressure Calibration by Resistance-Jump Transitions Up to 500 Kbar

  • K. J. Dunn
  • F. P. Bundy

Abstract

Equipment capable of generating the highest pressures usually consist of pistons of truncated-cone or truncated-pyramid geometry which are forced toward each other with their pressure faces against the pressure cell and the spaces between their tapered flanks filled, or partially filled, with a gasket material. Of the force applied to the base of a piston, the fractions which apply to the face against the pressure cell and to the flanks against the gasket are not only unknown, but also vary with the loading. Hence there is no accurate way of calculating the cell pressure from the force applied to the piston base in relation with the area of the pressure face. In such equipment the cell pressures may be calibrated in terms of the force applied to the pistons by monitoring selected calibration materials in the cell which have known physical behaviors with pressure, such as lattice compression, shifts of optical bands or lines, or resistivity jumps associated with first-order phase changes. Such pressure calibration is not absolute, and its accuracy depends upon the more basic, or absolute, calibration of the phenomena being monitored. A discussion of the various more or less absolute methods, and the history of pressure calibration of very high pressure equipment will not be presented here. Rather, the purpose of this article is to present information on, and techniques for using, materials wich have first-order resistance jumps in the 200-to-500-kbar ränge. In particular, these are the alpha-epsilon transitions in the iron-cobalt and the iron-vanadium alloys which were first observed in shock compression experiments and reported by Loree, et al. in 1966 [1].

Keywords

Shock Compression Cell Pressure Pressure Face Pressure Calibration High Pressure Equipment 
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.
    T. R. Loree, C. M. Fowler, E. G. Zukas, and F. S. Minshall, J. Appl. Phys. 37, 1918 (1966).CrossRefGoogle Scholar
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    F. P. Bundy, Rev. Sei. Instr. 46, 1318 (1975).CrossRefGoogle Scholar
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    D. Papantonis and W. A. Bassett, J. Appl. Phys. 48, 3374 (1977).CrossRefGoogle Scholar
  4. 4.
    K. J. Dunn and F. P. Bundy, this conference, Session B-l-D.Google Scholar
  5. 5.
    J. A. Van Vechten, Phys. Rev. B7, 1479 (1973).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1979

Authors and Affiliations

  • K. J. Dunn
    • 1
  • F. P. Bundy
    • 1
  1. 1.General Electric Corporate Research and DevelopmentSchenectadyUSA

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