Contribution of Energetically Reactive Surface Features to the Dissolution of CeO2 and ThO2 Analogues for Spent Nuclear Fuel Microstructures

In the safety case for the geological disposal of nuclear waste, the release of radioactivity from the repository is controlled by the dissolution of the spent fuel in groundwater. There remain several uncertainties associated with understanding spent fuel dissolution, including the contribution of energetically reactive surface sites to the dissolution rate. In this study, we investigate how surface features influence the dissolution rate of synthetic CeO2 and ThO2, spent nuclear fuel analogues that approximate as closely as possible the microstructure characteristics of fuel-grade UO2 but are not sensitive to changes in oxidation state of the cation. The morphology of grain boundaries (natural features) and surface facets (specimen preparation-induced features) was investigated during dissolution. The effects of surface polishing on dissolution rate were also investigated. We show that preferential dissolution occurs at grain boundaries, resulting in grain boundary decohesion and enhanced dissolution rates. A strong crystallographic control was exerted, with high misorientation angle grain boundaries retreating more rapidly than those with low misorientation angles, which may be due to the accommodation of defects in the grain boundary structure. The data from these simplified analogue systems support the hypothesis that grain boundaries play a role in the so-called “instant release fraction” of spent fuel, and should be carefully considered, in conjunction with other chemical effects, in safety performance assessements for the geological disposal of spent fuel. Surface facets formed during the sample annealing process also exhibited a strong crystallographic control and were found to dissolve rapidly on initial contact with dissolution medium. Defects and strain induced during sample polishing caused an overestimation of the dissolution rate, by up to 3 orders of magnitude

A general dose-response relationship for chronic chemical and other health stressors and mixtures based on an emergent illness severity model.

Current efforts to assess human health response to chemicals based on high-throughput in vitro assay data on intra-cellular changes have been hindered for some illnesses by lack of information on higher-level extracellular, inter-organ, and organism-level interactions. However, a dose-response function (DRF), informed by various levels of information including apical health response, can represent a template for convergent top-down, bottom-up analysis. In this paper, a general DRF for chronic chemical and other health stressors and mixtures is derived based on a general first-order model previously derived and demonstrated for illness progression. The derivation accounts for essential autocorrelation among initiating event magnitudes along a toxicological mode of action, typical of complex processes in general, and reveals the inverse relationship between the minimum illness-inducing dose, and the illness severity per unit dose (both variable across a population). The resulting emergent DRF is theoretically scale-inclusive and amenable to low-dose extrapolation. The two-parameter single-toxicant version can be monotonic or sigmoidal, and is demonstrated preferable to traditional models (multistage, lognormal, generalized linear) for the published cancer and non-cancer datasets analyzed: chloroform (induced liver necrosis in female mice); bromate (induced dysplastic focia in male inbred rats); and 2-acetylaminofluorene (induced liver neoplasms and bladder carcinomas in 20,328 female mice). Common- and dissimilar-mode mixture models are demonstrated versus orthogonal data on toluene/benzene mixtures (mortality in Japanese medaka, Oryzias latipes, following embryonic exposure). Findings support previous empirical demonstration, and also reveal how a chemical with a typical monotonically-increasing DRF can display a J-shaped DRF when a second, antagonistic common-mode chemical is present. Overall, the general DRF derived here based on an autocorrelated first-order model appears to provide both a strong theoretical/biological basis for, as well as an accurate statistical description of, a diverse, albeit small, sample of observed dose-response data. The further generalizability of this conclusion can be tested in future analyses comparing with traditional modeling approaches across a broader range of datasets

First-order/dissimilar-mode background (DMB), first-order/common-mode background (CMB), multi-stage, and lognormal DRFs versus data on chloroform-induced mild cellular liver necrosis in mice.

First-order/dissimilar-mode background (DMB), first-order/common-mode background (CMB), multi-stage, and lognormal DRFs versus data on chloroform-induced mild cellular liver necrosis in mice.</p