Journal of Materials Science

, Volume 45, Issue 8, pp 2106–2111 | Cite as

A novel approach to determine high temperature wettability and interfacial reactions in liquid metal/solid interface

  • Sascha Frenznick
  • Srinivasan Swaminathan
  • Martin Stratmann
  • Michael Rohwerder


In many technical processes, high temperature wetting of a liquid metal phase on a solid substrate occurs via an extensive chemical reaction and the formation of a new solid compound at the interface. For instance, good adhesion of the zinc coating to the steel surface is one of the most important requirements that the hot-dip galvanizing process has to fulfill. Good adhesion directly depends on the formation of a defect-free Fe2Al5 inhibition layer at the interface. The complex surface chemistry of oxides on the steel surface which is a result of segregation and selective oxidation upon recrystallization annealing significantly influences the kinetics of the correlated reactive wetting. This article presents the development of a novel advanced technique for the investigation of high temperature wetting process up to a temperature of 1100 K and provides first new insights in the mechanisms of the reactive wetting process in presence of oxides on the surface. The method is based on the sessile drop method with an additional spinning technique to get rid off the liquid metal phase at any chosen wetting time, thusly opening the way to access the interfacial reaction layer directly. The presented work focuses on model alloys of interest which are mainly relevant to the industrial steel grades. Emphasis is put both on the wettability of liquid Zn and on the interfacial reactions during reactive wetting process. Insights into such reactive phenomena are fundamental demand to improve the hot-dip galvanizability of advanced high strength steel grades.


Contact Angle Reaction Layer Avrami Exponent Annealing Atmosphere Reactive Wetting 
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 research was supported by the Research Program of the Research Fund for Coal and Steel, under contract RFS-CR-04021.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Sascha Frenznick
    • 1
  • Srinivasan Swaminathan
    • 1
  • Martin Stratmann
    • 1
  • Michael Rohwerder
    • 1
  1. 1.Max-Planck-Institut für Eisenforschung GmbHDüsseldorfGermany

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