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Section

Physical Sciences

Abstract

Accurate uranium provenance determination is crucial for nuclear non-proliferation and environmental protection, yet a forensic-level geochemical framework for Tanzanian uranium deposits has been lacking. This study establishes a robust analytical method using forty-five samples from three major deposits (Mkuju, Manyoni, and Bahi), analyzing elemental compositions, particularly Rare Earth Elements (REEs), via ICP-MS, and validating results using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Despite their proximity, the deposits exhibit distinct geochemical signatures as revealed by key REE ratios and anomalies. Mkuju (mean La/Ce 1.17, Nd/Ce 0.62, Sm/Nd 0.56) is characterized by a positive Europium (Eu) anomaly and heavy REE enrichment, indicating a high temperature, reducing hydrothermal origin. Manyoni (mean La/Ce 1.22, Nd/Ce 0.53, Sm/Nd 0.56) shows a strong positive Cerium (Ce) anomaly and a negative Eu anomaly, suggesting a redox-driven, oxidizing environment typical of sandstone- hosted deposits. Bahi (mean La/Ce 1.24, Nd/Ce 0.60, Sm/Nd 0.55) exhibits light REE enrichment with negative Ce and Eu anomalies, pointing to a complex secondary, redox-controlled model. Statistically, PCA showed a single dominant factor accounting for ∼90% of Mkuju's and 99% of Manyoni's variability, and HCA revealed a simple binary clustering for both, confirming a single-event, redox- controlled deposition. In contrast, Bahi's HCA showed three distinct clusters, reflecting multiple geological influences. Furthermore, the strong positive correlation between La/Ce and Eu/Eu∗ in Manyoni (r = 0.713) and moderate correlations in Mkuju (r = 0.592) and Bahi (r = 0.498) confirm a shared influence from geological history. This framework provides a powerful tool for nuclear forensics, with future work advised to integrate isotopic data (especially Pb isotopes) and machine learning for enhanced accuracy.

Included in

Nuclear Commons

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