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Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials. | LitMetric

AI Article Synopsis

  • Synthesis of new inorganic materials is challenging due to limited guidance on optimal solid-state procedures.
  • Researchers introduced primary and secondary competition metrics to assess how likely target materials will form compared to impurities in solid-state reactions.
  • They applied these metrics to analyze thousands of reactions, identifying efficient synthesis methods for barium titanate (BaTiO) that outperform traditional approaches by using unconventional precursors.

Article Abstract

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl and NaTiO) that produce BaTiO faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604012PMC
http://dx.doi.org/10.1021/acscentsci.3c01051DOI Listing

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