Development of a New Quantitative X-Ray Microanalysis Method for Electron Microscopy
Bibliographic record
Abstract
Quantitative X-ray microanalysis of thick samples is usually performed by measuring the characteristic X-ray intensities of each element in a sample and in corresponding standards. The ratio of the measured intensities from the unknown material to that from the standard is related to the concentration using the ZAF or ϕ(ρz) equations. Under optimal conditions, accuracies approaching 1% are possible. However, all the experimental conditions must remain the same during the sample and standard measurements. This is not possible with cold field emission scanning electron microscopes (FE-SEMs) where beam current can fluctuate around 5% in its stable regime. Very little work has been done on variable beam current conditions (Griffin, B.J. & Nockolds, C.E., Scanning 13, 307-312, 1991), and none relating to cold FE-SEM applications. To address this issue, a new method was developed using a single spectral measurement. It is similar in approach to the Cliff-Lorimer method developed for the analytical transmission electron microscope. However, corrections are made for X rays generated from thick specimens using the ratio of the characteristic X-ray intensities of two elements in the same material. The proposed method utilizes the ratio of the intensity of a characteristic X-ray normalized by the sum of X-ray intensities of all the elements measured for the sample, which should also reduce the amplitude of error propagation. Uncertainties in the physical parameters of X-ray generation are corrected using a calibration factor that must be previously acquired or calculated. As an example, when this method was applied to the calculation of the composition of Au-Cu National Institute of Standards and Technology standards measured with a cold field emission source SEM, relative accuracies better than 5% were obtained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".