Optimization of solid phase extraction chromatography for the separation of Np from U and Pu using experimental design tools in complex matrices
Bibliographic record
Abstract
Effective solid phase extraction separation methods of actinides and fission products are required in the control and evaluation of common or experimental nuclear spent fuel reprocessing strategies and environmental contaminated samples. In this study, we have developed a simpler sequential analytical separation scheme to isolate 237Np from U and Pu. Experimental design tools were used to achieve parameter optimization. We studied the contribution of critical factors such as the type of resin, acidity, sulfamic acid concentration and sample volume to actinide extraction with a multivariate approach. Following a sequential assembly approach, fractional factorial designs were used to select the best resin. Full factorial designs were used to evaluate the expected response for the chosen multifactorial space. After discarding a first order linear model, the designs were augmented and the response surface methodology was used to evaluate the response through the use of a quadratic model together with graphical and canonical analysis. Knowledge acquired from multiple actinide responses allowed us to find multi-criteria compromise solutions that were successfully applied for the separation of Np from Pu and U in complex matrices.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".