Determination of optimal metallic secondary target thickness, collimation, and exposure parameters for X‐ray tube‐based polarized EDXRF
Bibliographic record
Abstract
Tube‐based‐polarized energy‐dispersive X‐ray fluorescence (EDXRF) is a powerful adaptation on traditional EDXRF, requiring very specific geometry and a scattering target to generate polarized X‐rays. This secondary target is typically chosen to be a metallic foil, allowing for the polarization of the incident X‐ray beam, and the addition of the secondary target's fluorescence response to the initial beam. A simulation, using GEANT4 Monte Carlo code, and an experimental confirmation were used to determine the optimal thickness of a metallic secondary target for use in tube‐based‐polarized EDXRF. The optimal thickness was determined by looking at the signal‐to‐noise ratio (SNR). Using the results, the optimal thickness and tube potential were calculated for the common secondary target materials Cu, Mo, and Sn, when looking at an Fe sample. The optimal thickness results were compared with the results when using an ‘infinitely thick’ target. The results show improvements in SNRs of 6 − 17 % , illustrating the potential benefits of such calculations. Additionally, the optimal collimation of a polarized EDXRF system was examined, and it was found that increasing the total count rate should be the primary goal of geometrical optimization. If the count rate of the experimental setup is limited by tube output, then having the largest possible collimators yielded the maximum SNR. In contrast, if the count rate is limited by detector dead time, then decreasing the collimator size between secondary target and sample provided the maximal SNR. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".