arXiv subjects

Ericmoore Jossou

Publications and source records attributed to Ericmoore Jossou.

6 recordsLinked to original sources

arXiv2026

Design rules for industrial-scale sintering of UB4-UBC composites with high uranium density

Uranium borides are promising candidate fuel forms for use in advanced nuclear reactors due to their high thermal conductivity and potential for dual use as both fuel and burnable absorber materials. In this work, uranium tetraboride (UB$_4$) and uranium monoboroncarbide (UBC) composites were synthesized using an industrially scalable borocarbothermic reduction method. The high-temperature structural evolution of the as-synthesized borides was investigated using in situ synchrotron X-ray diffraction (SXRD). The oxidation behavior was further characterized using a combination of SXRD and thermogravimetric analysis (TGA), allowing direct comparison with other potential accident-tolerant fuels such as UB$_2$, U$_3$Si$_2$, UC, and UN. The UB$_4$-UBC composite shows higher uranium loading than monolithic UB$_4$ and demonstrates promising oxidation behavior at elevated temperature, pointing to its potential as an improved uranium boride-based fuel form.

cond-mat.mtrl-sci↗

arXiv2025

Chromium-doped uranium dioxide fuels: A review

UO2 doped with parts per million Cr2O3 powder is considered a potential near term accident tolerant fuel candidate. Here, the results of decades of industry and academic research into Cr-doped UO2 are analyzed and their shortcomings are critiqued. Focusing on the incorporation mechanisms of Cr into the fuel matrix, we explore a mechanistic understanding of the characteristic properties of Cr-doped UO2, notably, enhanced fission gas retention attributed to enlarged grain sizes following sintering, along with marginal improvements in the thermophysical properties. The findings of recent X-ray Adsorption Near Edge Spectroscopy studies were compared and put into conversation with historic data regarding the incorporation of Cr in UO2. On the basis of defect mechanisms, the case is made for the substitutional incorporation of Cr governing the lattice solubility but not the enhanced U diffusivity. Instead, Cr/Cr2O3 redox chemistry in a well-defined oxygen potential explains the differences in the U diffusivity and O/M ratio. The primary mechanism of doping-enhanced grain growth is found to be liquid assisted sintering due to a CrO(l) eutectic phase at the grain boundaries. The role of inhomogeneities in Cr concentration in UO2 at various length scales across the materials microstructure is highlighted and connected to promising experimental and modeling work to fill in the gaps in the current understanding of Cr-doped UO2. The review ends with an outline of future works that combine meticulous irradiation studies and high resolution experiments with next generation modeling and simulations techniques empowered by machine learning advances to accelerate the fabrication and adoption of Cr-doped UO2 light water reactors.

cond-mat.mtrl-sci↗

arXiv2025

Interlaced dynamic XCT reconstruction with spatio-temporal implicit neural representations

In this work, we investigate the use of spatio-temporalImplicit Neural Representations (INRs) for dynamic X-ray computed tomography (XCT) reconstruction under interlaced acquisition schemes. The proposed approach combines ADMM-based optimization with INCODE, a conditioning framework incorporating prior knowledge, to enable efficient convergence. We evaluate our method under diverse acquisition scenarios, varying the severity of global undersampling, spatial complexity (quantified via spatial information), and noise levels. Across all settings, our model achieves strong performance and outperforms Time-Interlaced Model-Based Iterative Reconstruction (TIMBIR), a state-of-the-art model-based iterative method. In particular, we show that the inductive bias of the INR provides good robustness to moderate noise levels, and that introducing explicit noise modeling through a weighted least squares data fidelity term significantly improves performance in more challenging regimes. The final part of this work explores extensions toward a practical reconstruction framework. We demonstrate the modularity of our approach by explicitly modeling detector non-idealities, incorporating ring artifact correction directly within the reconstruction process. Additionally, we present a proof-of-concept 4D volumetric reconstruction by jointly optimizing over batched axial slices, an approach which opens up the possibilities for massive parallelization, a critical feature for processing large-scale datasets.

eess.IV↗

arXiv2025

Multipurpose in situ cell design for 3D X-ray imaging of electrochemical processes

We present the design of a modular multipurpose cell for monitoring the degradation of materials in extreme environments. This cell decouples the reference electrode from the working and counter electrodes, permitting precise electrochemical control and measurement reliability. The design is compatible with 4th generation synchrotron light sources, and its emphasis on modularity facilitates adaptation to different beamlines, where there may be variations in sample stage requirements and X-ray imaging techniques. Experimental tests with the novel design demonstrate its support of real-time corrosion and hydrogen embrittlement measurements under both Bragg Coherent Diffraction Imaging (BCDI) and Dark Field X-ray Microscopy (DFXM) configurations.

physics.ins-det↗

arXiv2024

MSEG-VCUQ: Multimodal SEGmentation with Enhanced Vision Foundation Models, Convolutional Neural Networks, and Uncertainty Quantification for High-Speed Video Phase Detection Data

High-speed video (HSV) phase detection (PD) segmentation is crucial for monitoring vapor, liquid, and microlayer phases in industrial processes. While CNN-based models like U-Net have shown success in simplified shadowgraphy-based two-phase flow (TPF) analysis, their application to complex HSV PD tasks remains unexplored, and vision foundation models (VFMs) have yet to address the complexities of either shadowgraphy-based or PD TPF video segmentation. Existing uncertainty quantification (UQ) methods lack pixel-level reliability for critical metrics like contact line density and dry area fraction, and the absence of large-scale, multimodal experimental datasets tailored to PD segmentation further impedes progress. To address these gaps, we propose MSEG-VCUQ. This hybrid framework integrates U-Net CNNs with the transformer-based Segment Anything Model (SAM) to achieve enhanced segmentation accuracy and cross-modality generalization. Our approach incorporates systematic UQ for robust error assessment and introduces the first open-source multimodal HSV PD datasets. Empirical results demonstrate that MSEG-VCUQ outperforms baseline CNNs and VFMs, enabling scalable and reliable PD segmentation for real-world boiling dynamics.

cs.CV↗

arXiv2024

VideoSAM: A Large Vision Foundation Model for High-Speed Video Segmentation

High-speed video (HSV) segmentation is essential for analyzing dynamic physical processes in scientific and industrial applications, such as boiling heat transfer. Existing models like U-Net struggle with generalization and accurately segmenting complex bubble formations. We present VideoSAM, a specialized adaptation of the Segment Anything Model (SAM), fine-tuned on a diverse HSV dataset for phase detection. Through diverse experiments, VideoSAM demonstrates superior performance across four fluid environments -- Water, FC-72, Nitrogen, and Argon -- significantly outperforming U-Net in complex segmentation tasks. In addition to introducing VideoSAM, we contribute an open-source HSV segmentation dataset designed for phase detection, enabling future research in this domain. Our findings underscore VideoSAM's potential to set new standards in robust and accurate HSV segmentation. The code and dataset used in this study are available online at https://github.com/chikap421/videosam.

cs.CV↗