arXiv subjects

Ken Kurosaki

Publications and source records attributed to Ken Kurosaki.

5 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

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 $κ$. Using a curated dataset of 71,913 entries, we show that high-$ZT$ materials reside not only in the low-$κ$ regime but also cluster near a lattice-to-total thermal conductivity ratio ($κ_\mathrm{L}/κ$) 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 $κ$ and $κ_\mathrm{L}/κ$ for screening and guiding the optimization of TE materials. By applying this framework to 104,567 inorganic compounds, we identify 2,522 ultralow-$κ$ 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 $κ_\mathrm{L}/κ$ $\approx$ 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↗

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

Technical Overview of Recent Developments in Small Modular Reactors in the United States

Small modular reactors (SMRs) are a class of advanced nuclear fission reactors characterized by their compact core size (typically <300 MWe) and passive safety systems. Their modular design enables on-site assembly, making them suitable for deployment in locations inaccessible to conventional large-scale reactors. With rising global energy demand, particularly driven by the growth of AI, SMRs have recently gained attention as a potential solution for powering data centers. This technical review aims to provide the public and relevant stakeholders with a foundational understanding of SMR technology. It begins with an overview of SMR concepts, historical context, and their current role in the U.S. energy mix. Detailed technical summaries of nine selected SMR designs are then presented, covering core design, fuel systems, reactivity control, and safety features. The report also outlines key regulatory frameworks, including 10 CFR Part 50, Part 52, and the technology-inclusive, risk-informed, and performance-based framework currently under development. Finally, major U.S. programs and legislative efforts supporting SMR deployment over the past decade are summarized.

physics.soc-ph↗

arXiv2024

Topic Modeling and Sentiment Analysis on Japanese Online Media's Coverage of Nuclear Energy

Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understanding of public sentiment, is of paramount importance. While nationwide surveys have traditionally been used to gauge public views, the rise of social media in recent years has provided a promising new avenue for understanding public sentiment. To explore domestic sentiment on nuclear energy-related issues expressed online, we analyzed the content and comments of over 3,000 YouTube videos covering topics related to nuclear energy. Topic modeling was used to extract the main topics from the videos, and sentiment analysis with large language models classified user sentiments towards each topic. Additionally, word co-occurrence network analysis was performed to examine the shift in online discussions during August and September 2023 regarding the release of treated water. Overall, our results provide valuable insights into the online discourse on nuclear energy and contribute to a more comprehensive understanding of public sentiment in Japan.

cs.CL↗