Simultaneous secondary electron microscopy in the scanning transmission electron microscope with applications for <i>in situ</i> studies
Bibliographic record
Abstract
Scanning/transmission electron microscopy (STEM) is a powerful characterization tool for a wide range of materials. Over the years, STEMs have been extensively used for in situ studies of structural evolution and dynamic processes. A limited number of STEM instruments are equipped with a secondary electron (SE) detector in addition to the conventional transmitted electron detectors, i.e. the bright-field (BF) and annular dark-field (ADF) detectors. Such instruments are capable of simultaneous BF-STEM, ADF-STEM and SE-STEM imaging. These methods can reveal the 'bulk' information from BF and ADF signals and the surface information from SE signals for materials <200 nm thick. This review first summarizes the field of in situ STEM research, followed by the generation of SE signals, SE-STEM instrumentation and applications of SE-STEM analysis. Combining with various in situ heating, gas reaction and mechanical testing stages based on microelectromechanical systems (MEMS), we show that simultaneous SE-STEM imaging has found applications in studying the dynamics and transient phenomena of surface reconstructions, exsolution of catalysts, lunar and planetary materials and mechanical properties of 2D thin films. Finally, we provide an outlook on the potential advancements in SE-STEM from the perspective of sample-related factors, instrument-related factors and data acquisition and processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".