Data-driven on-site diagnostic technology: Predicting microbiologically influenced corrosion risk for ensuring long-term integrity (Contract research); FY2024 Nuclear Energy Science & Technology and Human Resource Development Project

Collaborative Laboratories for Advanced Decommissioning Science; Japan Agency for Marine-Earth Science and Technology*

The Collaborative Laboratories for Advanced Decommissioning Science (CLADS), Japan Atomic Energy Agency (JAEA), has been conducting the Nuclear Energy Science & Technology and Human Resource Development Project (hereafter referred to "the Project") from FY2019. The Project aims to contribute to solving problems in the nuclear energy field represented by the decommissioning of the Fukushima Daiichi Nuclear Power Station (1F), Tokyo Electric Power Company Holdings, Inc. (TEPCO). For this purpose, intelligence was collected from all over the world, and basic research and human resource development were promoted by closely integrating/collaborating knowledge and experiences in various fields beyond the barrier of conventional organizations and research fields. The sponsor of the Project was moved from the Ministry of Education, Culture, Sports, Science and Technology to JAEA since the newly adopted proposals in FY2018. On this occasion, JAEA constructed a new research system where JAEA-academia collaboration is reinforced and medium-to-long term research/development and human resource development contributing to the decommissioning are stably and consecutively implemented. Among the adopted proposals in FY2024, this report summarizes the research results of the "Data-driven on-site diagnostic technology: predicting microbiologically influenced corrosion risk for ensuring long-term integrity" conducted in FY2024. The present study aims to establish an innovative on-site diagnostic protocol capable of predicting microbiologically influenced corrosion (MIC) risks with high accuracy. To achieve this, we combined high-throughput analytical methods, simulated field experiments, and data-driven statistical analyses. Environmental samples were collected in Fukushima Prefecture to characterize microbial community structures and to develop key technologies for on-site genetic diagnostics. Using these samples, we evaluated the iron-corroding potential under various culture conditions and designed experimental systems simulating 1F environment. A high-throughput testing framework was validated for its ability to measure actual iron corrosion. In parallel, statistical approaches integrating microbial community profiles with MIC activity data were applied, yielding useful insights into both their applicability and limitations. Further, fixation methods for microbial observation in corrosion samples were examined, and optimal conditions were identified. Overall, the project provided valuable information on environmental microbial communities, cultivation of corrosion-associated microorganisms, and data-driven statistical approaches. These results lay the groundwork for the development of biomarker-based, ubiquitous diagnostic technologies for MIC applicable to diverse field environments, including those around 1F site.