Determination of 93Zr, 237Np and Th radionuclides in radioactive waste and mineral samples: extension of the method for determination of actinides
Bibliographic record
Abstract
Abstract A novel method has been developed for the simultaneous determination of difficult-to-measure (DTM) nuclides 93Zr, 237Np, 232Th, 230Th and 228Th using a DGA resin® (containing N,N,N′,N′-tetra-n-octyl-diglycolamide) column for simultaneous separation of Zr and actinides followed by purification of Zr, Np and Th on a TEVA resin® (containing Aliquat 336) column. Samples are destroyed by fusion with sodium hydroxide or by acid destruction using HNO3, HCl and HF. Pre-concentration procedure tailored to the extraction chromatography is based on co-precipitation of Zr and actinides and removal of alumina, silica, iron and calcium. The concentrations of 93Zr, 237Np and Th nuclides are determined by ICP-MS and α spectrometry, respectively. High chemical recoveries (≥ 73%) and separation factors were obtained for Zr, Np and Th both in evaporation concentrates of an NPP and various samples of mineral origin such as soil and concrete. The procedure can be easily combined with the simultaneous separation of other actinides (Pu, Am, U) using the same DGA column that has been described in our previous papers (Cassette et al. in Appl Radiat Isot 68:122–130, 2010 and Groska et al. in J Radioanal Nucl Chem 309(3):1145–1158, 2016).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".