Quantification of plutonium in nuclear, environmental, and biological samples: A review
Bibliographic record
Abstract
• We reviewed analytical strategies to separate, purify, and quantify plutonium. • We described emerging microextraction and cloud point extraction techniques. • We compared the analytical performances of those techniques. • We assessed the simplicity, sustainability, and detection capacity of reviewed techniques. • We described isotopic fingerprinting to trace plutonium origins and behaviors. The measurement of plutonium (Pu) isotopes is a complex and crucial task for nuclear science, energy production, national security, and environmental monitoring. Determining the isotopic fingerprint of plutonium provides valuable insights into the origin and behavior of radioactive materials in various environments. However, this process presents several analytical challenges. The first challenge is to obtain measurable plutonium samples from complex environmental matrices, with methods such as liquid–liquid extraction, chromatography, cloud point extraction, and microextraction offering varying levels of effectiveness. The second challenge is to accurately detect plutonium isotopes, which have diverse radioactive properties, including different decay types, half-lives, and energies, without distorting isotopic signatures. Analytical techniques for plutonium measurement include radiometric methods like alpha spectrometry and liquid scintillation counting, and mass spectrometry methods (such as inductively coupled plasma mass spectrometry), which provide highly sensitive isotope identification. In this review, we examine various pretreatment techniques for sample preparation, explore methods for efficient plutonium extraction, and compare the precision and accuracy of various instrumental approaches in isotopic fingerprinting.
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.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".