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Combinatorial Screening of Cuprate Superconductors by Drop-On-Demand Inkjet Printing
Albert Queraltó1, Juri Banchewski1, Adrià Pacheco1
1Institut de Ciència de Materials de Barcelona (ICMAB-CSIC), Campus UAB, 08193 Bellaterra, Catalonia, Spain.
ACS Applied Materials & Interfaces
|February 12, 2021
Summary
High-throughput experimentation using inkjet printing enables rapid discovery of new superconducting materials. This approach optimizes fabrication processes for rare-earth-based copper oxide (REBCO) films, accelerating material design.
Area of Science:
- Materials Science
- Superconductivity
- Chemical Engineering
Background:
- Combinatorial and high-throughput experimentation (HTE) are revolutionizing material design through data-driven approaches.
- Drop-on-demand inkjet printing (IJP) offers precise fabrication of compositionally graded materials for HTE.
- Rare-earth barium copper oxide (REBCO) superconductors are critical for advanced applications.
Purpose of the Study:
- To demonstrate the efficacy of inkjet printing for combinatorial synthesis of REBCO materials.
- To optimize the epitaxial growth of REBCO superconducting films using a novel transient liquid-assisted growth (TLAG) method.
- To establish an experimental strategy for generating large datasets for machine learning in material design.
Main Methods:
- Fabrication of compositionally graded Y1-xGdxBa2Cu3O7 samples using IJP.
- Computational design and experimental validation of sample homogeneity (EDX, XRD).
- In situ synchrotron growth experiments tailored for HTE.
Main Results:
- Successful combinatorial synthesis and characterization of REBCO materials.
- Demonstrated optimization of REBCO film epitaxial growth via TLAG.
- Validation of advanced characterization techniques for HTE.
Conclusions:
- Inkjet printing is a versatile platform for combinatorial material discovery and optimization.
- The presented HTE strategy accelerates the development of high-performance superconducting films.
- This approach facilitates the creation of datasets essential for machine learning-driven material design.
