Investigating the impact of internal standard heterogeneity on gold quantification with total reflection X‐ray fluorescence: A simulation study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Total reflection X‐ray Fluorescence (TXRF) is a powerful analytical tool with high detection sensitivity that has been applied to a variety of biological samples. While its ability to quantify gold nanoparticles (AuNPs) in cancer cells has been demonstrated, the extension to tissue slices would be of interest. To that end, the preservation of the underlying tissue microstructure requires samples to be measured as microtome slices. In this form, internal standard spiking is warranted. Thus, it is important to examine the impact of sample heterogeneity on the TXRF's quantification accuracy. To address these questions, a TXRF spectrometer along with 5 μm thin heterogeneous and homogeneous samples were modeled using TOPAS. The simulation model generated TXRF spectra which were then analyzed to obtain recovery rates of Au in both sample types. The results showed near 100% recovery regardless of the elemental spatial distribution in the samples. This provides insights into the quantification potential for AuNPs inside tumors that are histologically processed into thin tissue slices. In addition, this simulation toolkit provides the first practical means of modeling TXRF spectroscopy which will hopefully be of use to the TXRF community.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it