Uraninite (UO2) is the most abundant uranium ore mineral, its synthetic analog is the primary constituent of most nuclear fuels, it is the desired product of bioremediation strategies for uranium-contaminated soils and groundwaters, and it is of fundamental interest in basic and applied actinide science. The solubility and dissolution kinetics of uraninite depend heavily on the oxidation state of uranium, therefore understanding the mechanisms of UO2 oxidative corrosion is essential to predicting its chemical stability throughout the nuclear fuel cycle. Despite decades of research, a full molecular-scale understanding of uraninite corrosion is lacking due to a dearth of knowledge regarding the atomic and electronic structures of UO2 surfaces under both pristine and corroded conditions. We have used crystal truncation rod (CTR) diffraction to measure the surface and near-surface atomic structures of UO2 single crystal surfaces, and have shown that upon exposure to dry oxygen gas at ambient pressure and temperature, oxidation fronts proceed into the crystals, with interstitial oxygen atoms penetrating to depths of 30 angstrom or more. Our experimental results are broadly consistent with the theoretical predictions of our colleagues. The CTR method, while exemplary for determining the arrangement of atoms at surfaces and interfaces, is insensitive to oxidation states. Since the oxidation state of uranium is key in determining its fate, full understanding requires quantitative determination of this parameter. X-ray photoelectron spectroscopy (XPS) is ideally suited to this problem because it probes depths comparable to the oxidation fronts observed in our CTR experiments, and because it can be used to unambiguously distinguish between U(IV), U(V), and U(VI). We propose here to use XPS measurements conducted in the Radiochemical Annex at EMSL to complete the picture of early-stage oxidation mechanisms on UO2 surfaces. This research has broad relevance to materials, biogeochemical, and environmental sciences and will generate knowledge that allows for the refinement and validation of a wide variety of predictive models, covering subjects from material failure to nuclear waste disposal to environmental remediation to fundamental science.