Detritiation Technology Development for Environmental Protection
Bibliographic record
Abstract
Korea is operating 24 nuclear power plants and a highly advanced neutron application reactor HANARO (High-flux Advanced Neutron Application Reactor). In addition, Korea is designing a tritium storage and delivery system (SDS) for ITER. We have been developing detritiation and tritium storage technologies since the operation of Wolsong CANDU (Canada Deuterium-Uranium) station in 1983. The Wolsong Tritium Removal System (TRF) was designed to remove tritium generated in heavy water of the moderator and heat transport. Catalysts transfer tritium from the tritiated heavy water to gaseous tritiated deuterium. The hydrogen isotopes, including tritium, are transported to a cryogenic distillation system where the tritium is removed for safe storage. Conventional high-pressure storage tanks can be dangerous for the storage of radioactive tritium gas. We have been studying various kinds of metal hydride, such as titanium, zirconium cobalt, and depleted uranium. Titanium was proven to store tritium safely and efficiently for a long period of time. Zirconium cobalt, meanwhile, incorporates tritium safely and compactly, and temporarily holds large quantities that can be recovered easily under safe, controlled conditions. However owing to the disproportionation characteristics of zirconium cobalt, we are now developing depleted uranium hydride safe handling technologies. In this technical note, we present the details of the recent development progress of these tritium systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".