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Phase nucleation through confined spinodal fluctuations at crystal defects evidenced in Fe-Mn alloys

Author

Listed:
  • A. Kwiatkowski da Silva

    (Max-Planck-Institut für Eisenforschung)

  • D. Ponge

    (Max-Planck-Institut für Eisenforschung)

  • Z. Peng

    (Max-Planck-Institut für Eisenforschung)

  • G. Inden

    (Max-Planck-Institut für Eisenforschung)

  • Y. Lu

    (Ludwig-Maximilians-Universität München)

  • A. Breen

    (Max-Planck-Institut für Eisenforschung)

  • B. Gault

    (Max-Planck-Institut für Eisenforschung)

  • D. Raabe

    (Max-Planck-Institut für Eisenforschung)

Abstract

Analysis and design of materials and fluids requires understanding of the fundamental relationships between structure, composition, and properties. Dislocations and grain boundaries influence microstructure evolution through the enhancement of diffusion and by facilitating heterogeneous nucleation, where atoms must overcome a potential barrier to enable the early stage of formation of a phase. Adsorption and spinodal decomposition are known precursor states to nucleation and phase transition; however, nucleation remains the less well-understood step in the complete thermodynamic sequence that shapes a microstructure. Here, we report near-atomic-scale observations of a phase transition mechanism that consists in solute adsorption to crystalline defects followed by linear and planar spinodal fluctuations in an Fe-Mn model alloy. These fluctuations provide a pathway for austenite nucleation due to the higher driving force for phase transition in the solute-rich regions. Our observations are supported by thermodynamic calculations, which predict the possibility of spinodal decomposition due to magnetic ordering.

Suggested Citation

  • A. Kwiatkowski da Silva & D. Ponge & Z. Peng & G. Inden & Y. Lu & A. Breen & B. Gault & D. Raabe, 2018. "Phase nucleation through confined spinodal fluctuations at crystal defects evidenced in Fe-Mn alloys," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03591-4
    DOI: 10.1038/s41467-018-03591-4
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    Cited by:

    1. Ye Wei & Zirong Peng & Markus Kühbach & Andrew Breen & Marc Legros & Melvyn Larranaga & Frederic Mompiou & Baptiste Gault, 2019. "3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-19, November.

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