Advancements in immobilization of cesium and strontium radionuclides in cementitious wasteforms—A review
Bibliographic record
Abstract
Abstract The safe and secure encapsulation or immobilization of nuclear waste, particularly low to intermediate‐level waste (accounting for ∼97% of the total volume of nuclear waste), has been a significant concern. Consequently, numerous studies have been conducted on various materials such as ordinary Portland cement‐based, bitumen, and ceramics for the purpose of waste encapsulation/immobilization. However, these studies generally offer a broad overview of materials performance without focusing on specific radioisotopes of concern. Cesium (Cs) and strontium (Sr) are important radioactive nuclides to consider for encapsulation, but the existing studies on immobilizing these elements are fragmented and lack a comprehensive understanding. This critical review article offers a thorough qualitative and quantitative analysis to uncover the primary trends/knowledge gaps within the field. It comprehensively delves into waste classifications/management and leaching assessments, followed by an exploration of the immobilization performance and durability issues of various traditional and advanced cementitious materials including low‐temperature chemically bonded ceramics such as alkali‐activated matrices and Mg‒K phosphates for the immobilization of Cs and Sr. Furthermore, the review article provides fresh insights and perspectives, including recommendations for improvements, novel technologies, and future trends in this domain.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".