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August 24–27, 2026
Dallas, TX|Hilton Anatole
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Diversification and the common ground
Craig Piercycpiercy@ans.org
Who would have thought, just a few years ago, that we would see so many long-standing barriers to new nuclear development falling like dominoes? Public opinion, policy, regulatory reform, finance and investment, design maturity, nuclear fuel enrichment, and fuel fabrication capacity have all advanced with remarkable speed in the United States.
Conventional wisdom holds that the most effective way to scale up the nuclear supply chain is to do so strategically, matching investments to the needs of reactor developers.
Jie Lu, Junhao Luo, Shuangbao Shu, Yuzhong Zhang, Xianli Lang, Jingjing Chen
Nuclear Science and Engineering | Volume 200 | Number 10 | October 2026 | Pages 2181-2194
Research Article | doi.org/10.1080/00295639.2025.2575420
Articles are hosted by Taylor and Francis Online.
Monitoring defects such as voids and foreign inclusions in building walls is critical for ensuring structural safety. Conventional methods like infrared thermography suffer from environmental interference, impact-echo techniques lack precise localization, and ultrasonic testing offers limited visualization. To address these limitations, we design a wall defect detection system based on Compton backscattering imaging and conduct simulation studies using Geant4. The detection system employs a radiation source emitting a fan-beam collimated by a front collimator to scan the wall model. Backscattered photons generated via Compton scattering are then collimated by a rear collimator and detected by a 256 × 256 pixel NaI(Tl) scintillator array detector. The system reconstructs cross-sectional density distribution images from scattered photon counts. After acquiring multiple layers of scans, image correction, noise reduction (Gaussian and median filtering), and threshold segmentation are applied, followed by three-dimensional (3D) reconstruction using volume rendering to extract structural details of internal defects. Geant4 simulations demonstrate that the system accurately identifies defect materials in concrete walls, with a positional error of less than 2%, a 3D reconstruction resolution of 1.0 mm, and both volume and surface area reconstruction errors below 4%.