Summary
This paper presents a novel analytical approach combining X-ray micro-tomography with unsupervised machine learning to characterise internal voids in Restacked-Rod-Process Nb3Sn superconducting wires used in high-field magnets. The authors identified two distinct void populations with different origins and consequences: voids in the copper matrix associated with tin leakage and compromised thermal stability, and voids within Nb3Sn sub-elements that impair mechanical performance under electromagnetic stress. The methodology demonstrates potential for optimising RRP wire design and predicting electro-mechanical and electro-thermal behaviour.
UK applicability
Not applicable. This is fundamental materials science research on superconductor wire design for particle physics applications, without direct relevance to farming systems, soil health, nutrient density, or human nutrition.
Key measures
Void distribution, void morphology, void location (copper matrix vs. Nb3Sn sub-elements), electro-thermal and electro-mechanical properties of RRP wires
Outcomes reported
The study developed a combined X-ray micro-tomography and unsupervised machine learning tool to characterise void distribution and morphology in RRP Nb3Sn superconducting wires. Two void types were identified—those in the copper matrix (related to tin leakage and poor electro-thermal stability) and those in Nb3Sn sub-elements (detrimental to electro-mechanical performance).
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