Cloud Point Extraction of Plutonium in Environmental Matrixes Coupled to ICPMS and α Spectrometry in Highly Acidic Conditions
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
A new cloud point extraction procedure has been developed for the quantification of plutonium(IV) in environmental samples. The separation procedure can be either coupled to inductively coupled plasma mass spectrometry (ICPMS) or α spectrometry for plutonium quantification. The method uses a combination of selective ligand (P,P'-di(2-ethylhexyl) methanediphosphonic acid (H2DEH[MDP])) and micelle shielding by bromine formation to enable quantitative extraction of Pu in highly acidic solutions. Cross-optimization of all parameters (nonionic and ionic surfactant, chelating agent, bromate, bromide, and pH) led to optimal of the extraction conditions. Figures of merit of the method for the detection using α spectrometry and ICPMS are reported (limit of detection, limit of quantification, minimal detectable activity, and recovery). Quantitative extractions (>95%) were obtained for a wide variety of aqueous and digested samples (synthetic urine, wastewater, drinking water, seawater, and soil samples). The method features the first successful coupling between α spectrometry and cloud point extraction and is the first demonstration of CPE suitability with metaborate fusion as a sample preparation approach, techniques used extensively in nuclear industries.
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) | 0.005 | 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