High-throughput experimentation (HTE) is gathering attention in the field of materials science as a novel strategy that enables the accelerated discovery, development and optimization of materials. Combinatorial studies based on the HTE approach can be performed with chemical solution deposition (CSD) techniques such as drop-on-demand inkjet printing (DoD IJP) by fabricating complex-shape samples with locally-uniform or graded compositions, and high spatial precision that can be later used for parallel characterization of morphological, structural and functional properties.

In this work, we explored the HTE approach aiming to push forward the optimization of REBCO superconducting films prepared following the recently developed transient-liquid assisted growth chemical solution deposition (TLAG-CSD) method, where growth rates 100 times larger than with conventional means are reached. Combinatorial samples were fabricated by mixing different rare earth cuprate precursor solutions with DoD IJP. Computational methods were employed to achieve high sample homogeneity and successful merging of precursor inks. The composition of each combinatorial sample was confirmed by EDX and high-resolution XRD. Epitaxial growth studies were then performed for the fast identification of the best processing conditions, and we could get guides for growth parameters. We use the advantages of this strategy to fast analyze different compositions in one single step by in-situ XRD synchrotron radiation experiments. Finally, we combined this knowledge with machine learning approaches to widen the understanding and rapid optimization of epitaxial growth of high-temperature REBCO superconducting films.

 References:
[1] Soler et al. Nat. Comm. 11, 344 (2020).
[2] Rasi et al. J. Phys. Chem. C 124, 15574-15584 (2020).
[3] Queraltó et al. to be submitted.

Keywords: Combinatorial chemistry, High-throughput experimentation, Superconducting materials, Inkjet printing