New combinatorial chemistry approach in material science

Abstract

A new combinatorial approach is proposed. It is based on the preparation of crystallized samples with a progressive composition gradient using the melting or floating zone technique. It may be applied to metallic, high refractory, and glass materials and allows the rapid elucidation phase constitution and the variation of physical properties with composition and microstructure. The approach may be applied to a broad spectrum of materials and samples may be quite smaller. Four examples are presented herein: polyphased Al-Co alloys, a single crystal of yttrium oxide codoped by Er3+ and Yb3+, and a glass sample containing SiO2-Al2O3-CaO-Na2O-K2O. This approach constitutes a good and inexpensive experimental method for exploring industrial materials and for the theorical prediction of materials of interest.

We propose in this paper a new combinatorial approach to materials study that is based on the preparation of materials with a continuous composition gradient. Samples are prepared by a melting or floating zone technique. On the same sample, it is possible to describe the phases across a phase diagram and to determine physical properties-microstructure-thermal heating-composition relations for those physical properties that can be measured in situ. Four examples will be given: one concerns the direct reading of phases across a phase diagram, the second shows the possibility of relating the phase diagram in-situ measurements of selected physical properties, the third describes the evolution of a physical property and its dependence on composition in a ternary system, and the last corresponds to an easy extension of this approach to higher-order systems.

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Cohen-Adad, M.T., Laversenne, L., Goutaudier, C. et al. New combinatorial chemistry approach in material science. JPE 22, 379–385 (2001). https://doi.org/10.1361/105497101770332929

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Keywords

  • Combinatorial Approach
  • Peritectic Reaction
  • Yttrium Oxide
  • Composition Gradient
  • Compositional Gradient