RESUMEN
The design and optimization of new processing approaches for the development of rare earth cuprate (REBCO) high temperature superconductors is required to increase their cost-effective fabrication and promote market implementation. The exploration of a broad range of parameters enabled by these methods is the ideal scenario for a new set of high-throughput experimentation (HTE) and data-driven tools based on machine learning (ML) algorithms that are envisaged to speed up this optimization in a low-cost and efficient manner compatible with industrialization. In this work, we developed a data-driven methodology that allows us to analyze and optimize the inkjet printing (IJP) deposition process of REBCO precursor solutions. A dataset containing 231 samples was used to build ML models. Linear and tree-based (Random Forest, AdaBoost and Gradient Boosting) regression algorithms were compared, reaching performances above 87%. Model interpretation using Shapley Additive Explanations (SHAP) revealed the most important variables for each study. We could determine that to ensure homogeneous CSD films of 1 micron thickness without cracks after the pyrolysis, we need average drop volumes of 190-210 pl, and no. of drops between 5000 and 6000, delivering a total volume deposited close to 1 µl.
RESUMEN
Research involved in developing alternative energy sources has become a necessity to face global warming. In this context, superconductivity is an appealing solution to enhance clean electrical energy provided that lower production costs can be attained. By implementation of chemical solution deposition techniques and high-throughput growth methods, low-cost nanostructured epitaxial cuprate superconductors are timely candidates. Here, we present a versatile and tunable solution method suitable for the preparation of high-performance epitaxial cuprate superconducting films. Disregarding the renowned trifluoroacetate route, we center our focus on the transient liquid-assisted growth (TLAG) that meets the requirement of being a greener chemical process together with ultrafast growth rates beyond 100 nm/s. We developed a facile, fast, and cost-effective method, starting from the synthesis of metal-propionate powders of Y, Ba, and Cu of high purity and high yields, being the precursors of the fluorine-free solutions, which enable the chemical and microstructural nanoscale homogeneity of YBa2Cu3O7-x (YBCO) precursor films. These solutions present endured stability and enable precise tunability of the composition, concentration, porosity, and film thickness. Homogeneous precursor films up to thicknesses of 2.7 µm through eight layer multidepositions are demonstrated, thus establishing the correct basis for epitaxial growth using the fast kinetics of the TLAG process. YBCO films of 500 nm thickness with a critical current density of 2.6 MA/cm2 at 77 K were obtained, showing the correlation of precursor film homogeneity to the final YBCO physical properties.
RESUMEN
Transient liquid assisted growth (TLAG) is an ultrafast non-equilibrium growth process mainly governed by kinetic parameters, which are only accessible through fast in situ characterizations. In situ synchrotron X-ray diffraction (XRD) analysis and in situ electrical resistivity measurements are used to derive kinetic diagrams of YBa2 Cu3 O7- x (YBCO) superconducting films prepared via TLAG and to reveal the unique peculiarities of the process. In particular, diagrams for the phase evolution and the YBCO growth rates have been built for the two TLAG routes. It is shown that TLAG transient liquids can be obtained upon the melting of two barium cuprate phases (and not just one), differentiated by their copper oxidation state. This knowledge serves as a guide to determine the processing conditions to reach high performance films at high growth rates. With proper control of these kinetic parameters, films with critical current densities of 2-2.6 MA cm-2 at 77 K and growth rates between 100-2000 nm s-1 are reached. These growth rates are 1.5-3 orders of magnitude higher than those of conventional methods.
RESUMEN
Combinatorial and high-throughput experimentation (HTE) is achieving more relevance in material design, representing a turning point in the process of accelerated discovery, development, and optimization of materials based on data-driven approaches. The versatility of drop-on-demand inkjet printing (IJP) allows performing combinatorial studies through fabrication of compositionally graded materials with high spatial precision, here by mixing superconducting REBCO precursor solutions with different rare earth (RE) elements. The homogeneity of combinatorial Y1-xGdxBa2Cu3O7 samples was designed with computational methods and confirmed by energy-dispersive X-ray spectroscopy (EDX) and high-resolution X-ray diffraction (XRD). We reveal the advantages of this strategy in the optimization of the epitaxial growth of high-temperature REBCO superconducting films using the novel transient liquid-assisted growth method (TLAG). Advanced characterization methods, such as in situ synchrotron growth experiments, are tailored to suit the combinatorial approach and demonstrated to be essential for HTE schemes. The experimental strategy presented is key for the attainment of large datasets for the implementation of machine learning backed material design frameworks.