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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".