The objective of this paper is the development of ink-jet processing as a new technique for chemical solution deposition of YBCO coatings and patterns. Our research is mainly focused on the investigation and determination of the rheological parameters towards the printability of water-based inks in order to produce continuous YBCO coatings or multi-filamentary patterns on SrTiO₃ substrates. A 0.185 mol L⁻¹YBCO ink with a viscosity of 4.77 mPa s and a surface tension of 67.9 mN m⁻¹, resulting in a ratio Re/We^{1 /2} of 7.37, is developed. Its printing behaviour is further verified using a camera with strobed illumination to quantify the droplet velocity and volume. After optimization of the deposition parameters, a 350 nm thick YBCO coating showing preferential c-axis orientation could be grown on SrTiO₃. This layer exhibits a critical current of 0.67 MA cm⁻² at 77 K in self-field. Finally, the shape and dimensions of printed YBCO tracks were determined using optical microscopy and non-contact profilometry, showing 200 nm thick and 200 μm wide tracks.

Ligand-to-surface interactions are critical factors in surface and interface chemistry to control the mechanisms governing nanostructured colloidal suspensions. In particular, molecules containing carboxylate moieties (such as citrate anions) have been extensively investigated to stabilize metal, metal oxide and metal fluoride nanoparticles. Using YF3 nanoparti-cles as a model system, we show here the self-assembly of citrate-stabilized nanostructures (supraparticles) with a size tunable by temperature. Results from several experimental techniques and molecular dynamics simulations show that the self-assembly of nanoparticles into supraparticles is due to ionic bridges between different nanoparticles. These interac-tions were caused by cations (e.g. ammonium) strongly adsorbed onto the nanoparticle surface that also interact strongly with non-bonded citrate anions, creating ionic bridges in solution between nanoparticles. Experimentally, we observe self-assembly of nanoparticles into supraparticles at 25 °C and 100 °C. Interestingly, at high temperatures (100 °C) this citrate-bridge self-assembly mechanism is more efficient, giving rise to larger supraparticles. At low temperatures (5 °C), this mechanism is not observed and nanoparticles remain stable. Molecular dynamic simulations show that the free ener-gy of a single citrate bridge between nanoparticles in solution is much larger than the thermal energy and in fact is much larger than typical adsorption free energies of ions on colloids. Summarizing our experiments and simulations, we identify as key aspects of the self-assembly mechanism the requirement of NPs with a surface able to adsorb anions and cations and the presence of multidentate ions in solution. This indicates that this new ion mediated self-assembly mechanism is not specific of YF3 and citrate anions, as supported by preliminary experimental results in other systems.

In this work, we review recent progress achieved in the use of chemical solution deposition (CSD) based on fluorinated metalorganic precursors to grow superconducting REBa₂Cu₃O₇ (REBCO) films and coated conductors (CCs). We examine, first of all, the advances in optimizing the steps related to the solutions preparation, deposition and pyrolysis based on novel low-fluorine metalorganic solutions. We show that a new type of multifunctional colloidal solutions including preformed nanoparticles (NPs), can be used to introduce artificial pinning centers (APCs). We analyze how to disentangle the complex physico-chemical transformations occurring during the pyrolysis with the purpose of maximizing the film thicknesses. Understanding the nucleation and growth mechanisms is shown to be critical to achieve a fine tuning of the final microstructure, either using the spontaneous segregation or the colloidal solution approaches, and make industrially scalable this process. Advanced nanostructural studies have deeply modified our understanding of the defect structure and its genealogy. It is remarkable the key role played by the high concentration of randomly distributed and oriented BaMO₃ (M = Zr, Hf) NPs which enhance the concentration of APCs, such as stacking faults and the associated partial dislocations. Correlating the defect structure with the critical current density J_c (H,T,θ) allows to reach a tight control of the vortex pinning properties and to devise a general scheme of the vortex pinning landscape in the whole H–T phase diagram. We also refer to the outstanding recent achievements in enhancing the vortex pinning strength by shifting the carrier concentration in REBCO films towards the overdoped state, where the pinning energy is maximum and so, record values of critical current densities are achieved. This confirms the performance competitiveness of nanocomposite CCs prepared through the CSD route. We conclude with a short summary of the progress in scaling the CC manufacturing using fluorinated solutions.

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 Y_1–xGd_xBa₂Cu₃O₇ 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.