Two facets of the x-ray microanalysis at low voltage: the secondary fluorescence x-rays emission and the microcalorimeter energy-dispersive spectrometer
Bibliographic record
Abstract
The best spatial resolution, for a microanalysis with a scanning electron microscope (SEM), is achieved by using a low voltage electron beam. But the x-ray microanalysis was developed for high electron beam energy (greater than 10 keV). Also, the specimen will often contain light and medium elements and the analyst will have to use a mixture of K, L, and sometime M x-ray peaks for the x-ray microanalysis. With a mixture of family lines, it will be common to have secondary fluorescence x-rays emission by K-L and L-K interactions. The accuracy of the fluorescence correction models presently used by the analyst are not well known for these interactions. This work shows that the modified secondary fluorescence x-rays emission correction models can improve the accuracy of the microanalysis for K-L and L-K interactions. The general equation derived in this work allows the identification of three factors which influence the secondary fluorescence x-rays emission. The fluorescence production factor can be used to predict the importance of the secondary fluorescence x-rays emission. A large value of the fluorescence production factor indicates that a fluorescence correction is needed. Another disadvantage of using a low voltage is that there are more frequent occurrences of x-ray peaks overlap. A new microanalysis instruments that combines the high-spatial resolution and high-energy resolution for x-ray detection is needed. The microcalorimeter energy-dispersive spectrometer (uEDS) should improve the low voltage microanalysis, but the maturity of this technology has to be evaluated first. One of the first commercial uEDS for x-ray microanalysis in a SEM is studied and analyzed in this work. This commercial uEDS has an excellent energy resolution (15 eV) and can detect x-rays of low energy. This x-ray detector can be used as a high-spatial resolution and high-energy resolution microanalysis instrument. There are still hurdles that this technology must overcome before i
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".