Examples of Charging Effects on the Spectral Quality of X‐ray Microanalysis on a Glass Sample Using the Variable Pressure Scanning Electron Microscope
Bibliographic record
Abstract
The performance of X-ray microanalysis in the variable pressure or environmental scanning electron microscope (VP-SEM or ESEM) is limited by skirting. Under certain conditions, charging effects can occur and change the X-ray emission, which affects the X-ray microanalysis. The conventional way to evaluate charging is to calculate the Duane-Hunt limit by fitting the X-ray intensity region located below the energy cut-off. Nevertheless, this method appears to have serious limitations for instance in the case of strong insulators. A perfect example of this limitation is to study the evolution of composition of an alkali glass with time. This paper reports on the evolution of the sodium X-ray intensity with time depending on accelerating voltage, pressure and presence of a surface coating. For certain conditions, a decrease of sodium X-ray intensity with time was observed but for other conditions the reverse behavior was noticed. The increase of sodium X-ray intensity with time was obtained when the force created by the surface electrons was stronger than the force generated by electrons trapped in the interaction volume, whereas the decrease of sodium X-ray intensity occurred when the force generated by electrons trapped in the interaction volume was the stronger. The variations of sodium X-ray intensity were also compared to the variation of the Duane-Hunt limit, the determination of which is studied in detail in this article.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".