arXiv subjects

Yuji Ohishi

Publications and source records attributed to Yuji Ohishi.

3 recordsLinked to original sources

arXiv2026

Thermophysical and mechanical properties of UFe$_2$ fabricated by spark plasma sintering

Following the accident at the Fukushima Daiichi Nuclear Power Plant in 2011, core meltdown produced fuel debris whose safe retrieval and management require reliable thermophysical and mechanical property data. Among the metallic phases identified in the debris, the U-Fe system is particularly important because of the abundant iron originating from in-vessel stainless steel structures. However, within this system, the high-temperature thermophysical properties of UFe$_2$ have received relatively little attention, with most prior studies focusing on its magnetic and electronic properties. To fill this data gap in the literature, we fabricated dense, nearly single-phase polycrystalline UFe$_2$ by arc melting followed by spark plasma sintering, and characterized its thermal and mechanical properties from room temperature to 1073 K. Results show that the thermal conductivity of UFe$_2$ increased monotonically from 10 Wm$^{-1}$K$^{-1}$ at 306 K to 25 Wm$^{-1}$K$^{-1}$ at 1073 K, surpassing those of the iron intermetallics Fe$_2$Zr and Fe$_2$B at high temperatures. In addition, UFe$_2$ is mechanically more compliant, displaying a Young's modulus $E$ of 69 GPa, a shear modulus $G$ of 24 GPa, and a Vickers hardness $H_{\mathrm{V}}$ of 5.6 GPa, all well below those of both Fe intermetallics. Consequently, during decommissioning, thermal-management and structural evaluations should take into account the comparatively high-conductivity and mechanically compliant nature of UFe$_2$ within the heterogeneous fuel debris.

cond-mat.mtrl-sci↗

arXiv2026

Thermophysical properties of spark plasma sintered UCo: a comparison with machine learning predictions

Uranium dioxide has been widely used as a nuclear fuel in commercial light water reactors due to its high uranium density and chemical stability. However, its relatively low thermal conductivity is not optimal from the viewpoints of fuel integrity and safety margins, particularly during loss-of-coolant accidents. Although the development of accident-tolerant fuels with higher thermal conductivity is strongly desired, many potential uranium compounds remain unexplored due to constraints associated with handling radioactive materials. To efficiently screen promising uranium compounds with high thermal conductivity, past studies have leveraged machine-learning models to accelerate the discovery process. In this study, we experimentally examine the model's predictions by fabricating UCo and measuring its high-temperature thermophysical properties. Our results show that the thermal conductivity of UCo predicted by machine learning is in good agreement with the experimental measurements. Despite slight discrepancies, additional SHAP analysis suggests that the model's decision logic is consistent with known physical trends. Overall, this study fills a gap in reported thermophysical properties of UCo and provides experimental support for machine-learning-assisted screening of uranium compounds relevant to advanced fuel development.

cond-mat.mtrl-sci↗

arXiv2025

Lattice-to-Total Thermal Conductivity Ratio: A Phonon-Glass Electron-Crystal Descriptor for Data-Driven Thermoelectric Design

Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on identifying compounds with low thermal conductivity $\kappa$. Using a curated dataset of 71,913 entries, we show that high-$ZT$ materials reside not only in the low-$\kappa$ regime but also cluster near a lattice-to-total thermal conductivity ratio ($\kappa_\mathrm{L}/\kappa$) of approximately 0.5. This optimal ratio provides a quantitative descriptor for the well-known phonon-glass electron-crystal (PGEC) design concept. Building on this insight, we construct a framework consisting of two machine learning models for the lattice and electronic components of thermal conductivity that jointly provide both $\kappa$ and $\kappa_\mathrm{L}/\kappa$ for screening and guiding the optimization of TE materials. By applying this framework to 104,567 inorganic compounds, we identify 2,522 ultralow-$\kappa$ candidates while simultaneously evaluating their proximity to the optimal PGEC regime. A follow-up case study on chemical doping demonstrates how the framework can qualitatively provide optimization strategies that shift pristine materials toward the ideal $\kappa_\mathrm{L}/\kappa$ $\approx$ 0.5 target. Ultimately, by integrating rapid screening with PGEC-guided optimization, our data-driven framework takes a critical step towards closing the gap between materials discovery and performance enhancement.

cond-mat.mtrl-sci↗