Particle size analysis in ferrofluids

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Abstract

In this paper we examine the applicability of the Gaussian and lognormal probability functions to describe the distribution of particle sizes found in ferrofluids. Measurements have been made of the particle size distributions contained in a large number of ferrofluids prepared by different techniques. From these measurements we conclude that the form of the distribution may be associated with the technique of particle preparation.

References (7)

  • R.W. Chantrell, private...
  • R.W. Chantrell et al.

    IEEE Trans. Magn. MAG-14

    (1978)
  • J. Shimoiisaka

    Japan. pat. no. 52000782

    ((1976). 1145 1134 V 2)
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