K<sub>α</sub> transition probabilities for platinum and uranium ions for possible X-ray biomedical applications<sup>1</sup>This article is part of a Special Issue on the 10th International Colloquium on Atomic Spectra and Oscillator Strengths for Astrophysical and Laboratory Plasmas.
Bibliographic record
Abstract
Platinum compounds, such as cisplatin and other high-Z materials, are increasingly common in biomedical applications. The absorption and emission of high-energy X-rays can occur via the 1s–2p Kα transitions in ions of heavy elements involving deep inner-shells. Oscillator strengths (f), line strengths (S), and radiative decay rates (A), for the 1s–2p transitions for the nine ionic states from hydrogen-like to fluorine-like, are presented for platinum and uranium. For platinum ions the Kα transitions are found to be in the hard X-ray region, 64–71 keV (0.18–0.17 Å), and for uranium ions they are in the range 94–105 keV (0.12–0.13 Å). Since the number of electrons in each ionic state of the element is different, the number of Kα transitions varies considerably. While there are two 1s–2p transitions (1s 2S1/2–2p [Formula: see text]) in H-like ions, there are 2, 6, 2, 14, 35, 35, and 14 transitions in He-like, Li-like, Be-like, B-like, C-like, N-like, and O-like ions, respectively, for a total of 112 Kα transitions for each element. These include both types of electric dipole (E1) allowed transitions, same-spin multiplicity and intercombination. The former dipole allowed transitions are in general strong; their radiative decay rates are of the order of A ∼ 1016 s–1. However, there are also many weaker transitions. We demonstrate the importance of these Kα transitions, as they appear as resonances in photo-ionization, which is relevant to the enhanced production of Auger electrons for possible radiation diagnostics and therapy.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 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".