Combined laser-based X-ray fluorescence and particle-induced X-ray emission for versatile multi-element analysis
Bibliographic record
Abstract
Abstract Particle and radiation sources are widely employed in manifold applications. In the last decades, the upcoming of versatile, energetic, high-brilliance laser-based sources, as produced by intense laser–matter interactions, has introduced utilization of these sources in diverse areas, given their potential to complement or even outperform existing techniques. In this paper, we show that the interaction of an intense laser with a solid target produces a versatile, non-destructive, fast analysis technique that allows to switch from laser-driven PIXE (Particle-Induced X-ray Emission) to laser-driven XRF (X-ray Fluorescence) within single laser shots, by simply changing the atomic number of the interaction target. The combination of both processes improves the retrieval of constituents in materials and allows for volumetric analysis up to tens of microns and on cm2 large areas up to a detection threshold of ppms. This opens the route for a versatile, non-destructive, and fast combined analysis technique.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".