Literature DB >> 33949743

Observing and Modeling the Sequential Pairwise Reactions that Drive Solid-State Ceramic Synthesis.

Akira Miura1, Christopher J Bartel2,3, Yosuke Goto4, Yoshikazu Mizuguchi4, Chikako Moriyoshi5, Yoshihiro Kuroiwa5, Yongming Wang6, Toshie Yaguchi7, Manabu Shirai7, Masanori Nagao8, Nataly Carolina Rosero-Navarro1, Kiyoharu Tadanaga1, Gerbrand Ceder2,3, Wenhao Sun9.   

Abstract

Solid-state synthesis from powder precursors is the primary processing route to advanced multicomponent ceramic materials. Designing reaction conditions and precursors for ceramic synthesis can be a laborious, trial-and-error process, as heterogeneous mixtures of precursors often evolve through a complicated series of reaction intermediates. Here, ab initio thermodynamics is used to model which pair of precursors has the most reactive interface, enabling the understanding and anticipation of which non-equilibrium intermediates form in the early stages of a solid-state reaction. In situ X-ray diffraction and in situ electron microscopy are then used to observe how these initial intermediates influence phase evolution in the synthesis of the classic high-temperature superconductor YBa2 Cu3 O6+ x   (YBCO). The model developed herein rationalizes how the replacement of the traditional BaCO3 precursor with BaO2 redirects phase evolution through a low-temperature eutectic melt, facilitating the formation of YBCO in 30 min instead of 12+ h. Precursor selection plays an important role in tuning the thermodynamics of interfacial reactions and emerges as an important design parameter in planning kinetically favorable synthesis pathways to complex ceramic materials.
© 2021 The Authors. Advanced Materials published by Wiley-VCH GmbH.

Entities:  

Keywords:  YBazzm3219902Cuzzm3219903Ozzm3219906+zzm321990x; ab initio thermodynamics; ceramics; phase evolution; predictive synthesis; solid-state synthesis

Year:  2021        PMID: 33949743     DOI: 10.1002/adma.202100312

Source DB:  PubMed          Journal:  Adv Mater        ISSN: 0935-9648            Impact factor:   30.849


  3 in total

1.  A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis.

Authors:  Matthew J McDermott; Shyam S Dwaraknath; Kristin A Persson
Journal:  Nat Commun       Date:  2021-05-25       Impact factor: 14.919

2.  A Flexible Method to Fabricate Exsolution-Based Nanoparticle-Decorated Materials in Seconds.

Authors:  Zhu Sun; Weiwei Fan; Yu Bai
Journal:  Adv Sci (Weinh)       Date:  2022-02-20       Impact factor: 17.521

3.  Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions.

Authors:  Haoyan Huo; Christopher J Bartel; Tanjin He; Amalie Trewartha; Alexander Dunn; Bin Ouyang; Anubhav Jain; Gerbrand Ceder
Journal:  Chem Mater       Date:  2022-08-05       Impact factor: 10.508

  3 in total

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