OPTIMIZATION AND VALIDATION OF A CHEMICAL PROCESS FOR URANIUM, MERCURY AND CESIUM LEACHING FROM CEMENTED RADIOACTIVE WASTES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Canadian Nuclear Laboratories (CNL) is developing a treatment and long-term management strategy for a legacy cemented radioactive waste that contains uranium, mercury, and fission products. Extracting the uranium would be advantageous for decreasing the waste classification and reducing the cost of long-term management. The chemical leachability of 3 key elements (U, Hg, and Cs) from a surrogate cemented waste (SCW) was studied with several lixiviants. The results showed that the most promising approach to leach and recover U, Hg, and Cs is the direct leaching of the SCW with H 2 SO 4 in strong saline media. Operating parameters such as particle size, temperature, pulp density, leaching time, acid and salt concentrations, number of leaching/washing steps, etc. were optimized to improve key elements solubilization. Sulfuric leaching in saline media of a SCW (U5) containing 1182 ppm of U, 1598 ppm of Hg, and 7.9 ppm of Cs in the optimized conditions allows key elements solubilisation of 98.5 ± 0.4%, 96.6 ± 0.1%, and 93.8 ± 1.1% of U, Hg, and Cs, respectively. This solubilization process was then applied in triplicate to 7 other SCWs prepared with different cements, liquid ratios, and at different aging times and temperatures. Concentrated sulfuric acid is added to the slurry until the pH is about 2, which causes the complete degradation of cement and the formation of CaSO 4 . Sulfuric acid is particularly useful because it produces a leachate that is amenable to conventional ion exchange technology for the separation and recovery of uranium.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it