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Unveiling the hidden risk: Cryo-TRLFS identifies critically elusive UO2 alteration phases under chemically complex
Huiyang Mei1, Noboru Aoyagi2, Seiya Takaki3
1Nuclear Professional School, School of Engineering, The University of Tokyo, 2-22 Shirakata Shirane, Tokai-mura, Ibaraki 319-1188, Japan.
None:
The safe management of nuclear materials, from Fukushima fuel debris to geological spent nuclear fuel (SNF) repositories, requires an accurate understanding and prediction of uranium (U) release risks. However, this is impeded by the difficulty in detecting the trace and amorphous phases formed during SNF alteration under chemically complex conditions, which critically control the initial reactivity of SNF. Here, the long-term alteration of UO2, a host phase of SNF and one of the major phases of fuel debris, was investigated under oxidizing conditions with key factors: boric acid (for criticality control), H2O2 (simulating radiolysis), seawater, and cement leachates. After experimental duration for five months, alteration products and synthetic U(VI) reference minerals were characterized using X-ray diffraction (XRD) and cryogenic time-resolved laser fluorescence spectroscopy (Cryo-TRLFS). XRD only identified trace metaschoepite in systems with deionized water, boric acid, and H2O2 solutions, but failed for the identification of the complex seawater and cement leachate systems. Cryo-TRLFS succeeded in detecting well-resolved spectral fingerprints for all these samples. Parallel factor analysis further decomposed two distinct U(VI)-bearing fluorescent components with different fluorescence lifetimes (56 ± 5 and 242 ± 60 μsec), both attributable to metaschoepite, revealing speciation details inaccessible to XRD. This study demonstrates the critical advantage of Cryo-TRLFS in identifying the elusive, U(VI) phases of interest for risk assessment. The findings directly inform the safety protocols for Fukushima fuel debris retrieval by clarifying initial alteration products, and provide essential mechanistic data to advance predictive models for U(VI) speciation evolution in repository environments, ultimately contributing to more robust long-term safety assessments.
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