<b>Evolution in X-ray analysis from micro to atomic scales in aberration-corrected scanning transmission electron microscopes</b>
Bibliographic record
Abstract
X-ray analysis is one of the most robust approaches to extract quantitative information from various materials and is widely used in various fields ever since Raimond Castaing established procedures to analyze electron-induced X-ray signals for materials characterization '70 years ago'. The recent development of aberration-correction technology in a (scanning) transmission electron microscopes (S/TEMs) offers refined electron probes below the Å level, making atomic-resolution X-ray analysis possible. In addition, the latest silicon drift detectors allow complex detector arrangements and new configurational designs to maximize the collection efficiency of X-ray signals, which make it feasible to acquire X-ray signals from single atoms. In this review paper, recent progress and advantages related to S/TEM-based X-ray analysis will be discussed: (i) progress in quantification for materials characterization including the recent applications to light element analysis, (ii) progress in analytical spatial resolution for atomic-resolution analysis and (iii) progress in analytical sensitivity toward single-atom detection and analysis in materials. Both atomic-resolution analysis and single-atom analysis are evaluated theoretically through multislice-based calculation for electron propagation in oriented crystalline specimen in combination with X-ray spectrum simulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".