Deep geological repositories — A review of design concepts, near-field evolution, and their implications for nuclear waste containment
Bibliographic record
Abstract
Effective nuclear waste disposal is crucial for strengthening public confidence in nuclear energy as a cornerstone of sustainable, large-scale, carbon-neutral energy generation. Deep geological repositories (DGRs) provide the most viable long-term solution, employing a multi-barrier isolation system that inhibits radionuclide release and migration through a combination of engineered and natural barriers. While extensive research has focused on radionuclide transport mechanisms and the influence of geochemical interactions within host rock formations, a comprehensive understanding of the near-field environment, its physicochemical evolution, and its implications for repository design and nuclear waste management decisions remains underexplored. This review critically examines DGR concepts, near-field components, and processes, with a particular focus on canister-bentonite interactions, corrosion evolution, bentonite self-sealing mechanisms, and hydrogen gas generation. A critical assessment of experimental, numerical, and full-scale studies, highlights the complexities of coupled thermal, hydraulic, mechanical, and chemical (THMC) processes that govern repository evolution. Key challenges include scaling laboratory findings to field-scale repository conditions, integrating microbial and radiation-driven interactions, and refining long-term predictive models for corrosion and radionuclide migration. Addressing these gaps is critical for advancing repository safety assessments, optimizing engineered barrier systems, and ensuring the long-term stability of nuclear waste disposal strategies. These insights are expected to contribute to the ongoing development of robust and globally implementable waste management solutions, reinforcing nuclear energy's role in climate change mitigation and the transition to a net-zero carbon economy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".