In situ MgB₂ superconducting samples were prepared by using the spark plasma sintering method. The density of the obtained bulks was up to 95% of the theoretical value predicted for the material. The structural and microstructural characterizations of the samples were investigated using X-ray diffraction and SEM and correlated to their superconducting properties, in particular their critical current densities, J_c, which was measured at 20 K. Extremely high critical current densities of up to 6.75 × 10⁵ A/cm² in the self-field and above 10⁴ A/cm² at 4 T were measured at 20 K, indicating that vortex pinning is very strong. This property is mainly attributed to the sample density and MgB₂ nanograins in connection to the presence of MgO precipitates and areas rich in boron.

This study aims to investigate the sintering mechanisms of magnesium diborides using spark plasma sintering and optimize the sintering process conditions. Three different precursor materials were used: (i) commercial powder MgB₂, (ii) mixtures of magnesium and nano boron powder (Mg + 2B), and (iii) mixtures of Mg and MgB₄ powder for an ‘in-situ’ reactive synthesis. The density of the obtained bulks exceeds 90% of the theoretical density of the material. The structure and microstructure of the samples were analyzed using x-ray diffraction and scanning electron microscope, which were correlated with their superconducting properties. The critical current density (J _c), pinning force (f _p), and electrical resistivity (ρ) of the samples were also investigated. The results show that the in-situ reactive synthesis of MgB₂ using magnesium and nano boron powder was the most effective method for producing high-quality samples with improved superconducting properties. The findings could lead to the development of more efficient methods for producing high-performance of MgB₂ bulks.

The advent of superconducting bulks, due to their compactness and performance, offers new perspectives and opportunities in many applications and sectors, such as magnetic field shielding, motors/generators, NMR/MRI, magnetic bearings, flywheel energy storage, Maglev trains, among others. The investigation and characterization of bulks typically relies on time-consuming and expensive experimental campaigns; hence the development of effective surrogate models would considerably speed up the research progress around them. In this study, we first produced an experimental dataset containing the levitation and lateral forces between different MgB₂ bulks and one permanent magnet under different operating conditions. Next, we have exploited the dataset to develop surrogate models based on Artificial Intelligence (AI) techniques, namely Extremely Gradient Boosting, Support Vector Regressor (SVR), and Kernel Ridge Regression. After the tuning of the hyperparameters of the AI models, the results demonstrated that SVR is the superior technique and can predict levitation and lateral forces with a worst-case accuracy scenario 99.86% in terms of goodness of fit to experimental data. Moreover, the response time of these models for the estimation of new datapoints is ultra-fast.

The critical current of superconducting materials, such as magnesium diboride (MgB₂) bulk superconductors, is a key parameter influencing their performance in various applications, including magnetic field shielding, MRI, and Maglev systems. Spark plasma sintering (SPS) is one of the most efficient methods to fabricate high-quality MgB₂, significantly saving fabrication time and controlling grain growth. The fabrication conditions, including temperature, pressure, and dwell time, can affect the critical current density. Traditional methods for estimating critical currents are time-consuming and costly. This study explores the use of advanced artificial intelligence (AI) techniques to develop accurate and efficient models for predicting the critical current in MgB₂ bulks with respect to 10 different influential fabrication properties and physical conditions. By using AI algorithms such as Gaussian process regression, extremely gradient boosting, and generalized regression neural network (GRNN) an extremely high accuracy in predictions against the actual experimental data was achieved. By defining and studying the extrapolation scenario, this study goes beyond of simple AI-based estimator model that performs well only within the training range of data. The developed AI models not only reduce the need for extensive experimental campaigns but also offer real-time prediction capabilities, paving the way for faster advancements in the research and development of superconducting technology. Overall, GRNN model demonstrated a good performance for both interpolation and extrapolation tasks with an R-squared of 0.999958 and 0.99521, respectively.

This rapid communication highlights a remarkable achievement obtained with a 120 mm diameter large cylindrical MgB₂ bulk: the measurement of magnetic levitation forces up to 700 N at 20 K, levitation being stable up to levitation forces exceeding 400 N. The investigated sample, which has one of the largest dimensions reported in the literature, was fabricated using the unconventional spark plasma sintering technique. This method is known as one of the most efficient methods for producing dense MgB₂ samples. This breakthrough holds significant potential for advancing high-performance levitation systems and superconducting applications, including high-speed transportation systems and magnetic bearings.

This work reports the progress in the preparation of superconducting and thermoelectric lamellar compounds processed by the unconventional Spark Plasma Sintering (SPS). The SPS equipment was modified with the aim of obtaining the textured and dense superconductor Bi₂Sr₂Ca₂Cu₃O_10,p-type oxide thermoelectric bulk as Ca₃Co₄O₉ and Ca_3-xAg_xCo₄O₉/Ag composites respectively. The new process is referred to as Spark Plasma Texturing (SPT). During SPT, the bulk material can freely deform. As a result, inter-grain preferential crystallographic orientation is created. The series of sintered and textured samples using the same Ag content were processed respectively. From the results, we can evidence: (i) the magnetic and/or structural transition around 350 °C, for both series of samples. (ii) The electrical resistivity (ρ) decreases with increasing Ag-substituted or Ag-added. (iii) The Seebeck coefficient (S) of the textured series is higher than that of the sintered series. In the case of the Ag-substituted, S, decreases with Ag content. The optimized composite is found to be Ca_2.6Ag_0.4Co₄O₉/8wt% Ag. We can note the remarkable reduction of ρ, and the improvement of power factor values up to 360 μW.m⁻¹.K⁻².The superconducting properties of single phased Bi₂Sr₂Ca₂Cu₃O₁₀ (Bi2223) consolidated using SPS and SPT will also be discussed.