Determination of isotope rations in ambient air at atmospheric pressure for nuclear forensics using LIBS
Bibliographic record
Abstract
Field portable technologies and methods are sought to provide simple, inexpensive, and fast analysis of materials in the mining, construction, and other industries. However, the level of portability needed for this particular application imposes some restrictions on the choice of many of the core components used in low-cost LIBS handheld sensor. This means that relatively low-performance components, such as a low-energy laser source and a low cost, low resolution spectrometer, and working conditions in ambient aire at atmospheric pressure must be considered to fulfil these conditions. In addition, the market price of such a portable device should be affordable or as low as possible to increase the breadth of potential end users and allow the deployment of multiple units for security enhancement. The present paper describes the determination of isotope rations using Laser-Induced Breakdown Spectroscopy in air at atmospheric pressure for partially resolved uranium-235/uranium-238 and hydrogen/deuterium isotope shift lines in such conditions. Using a Partial Least Square (PLS1) regression, it is possible to build a model that enables the accurate determination of the isotopic ration under conditions where the application of traditional univariate approaches for hydrogen and uranium would not be achievable without the use of ultra high resolution spectrometer. In addition, the application of PLS1 regression to determine the uranium-235/uranium-238 and deuterium/hydrogen isotopic rations between 0 and 1 mass fraction was also successfully demonstrated. The performance obtained with such a LIBS sensor will be discussed for nuclear forensics.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".