Dark-Field Imaging of Thin Specimens with a Forescatter Electron Detector at Low Accelerating Voltage
Bibliographic record
Abstract
A forescatter electron detector (FSED) was used to acquire dark-field micrographs (DF-FSED) on thin specimens with a scanning electron microscope. The collection angles were adjusted with the detector distance from the beam axis, which is similar to the camera length of the scanning transmission electron microscope annular DF detectors. The DF-FSED imaging resolution was calculated with SMART-J on an aluminum alloy and carbon nanotubes (CNTs) decorated with platinum nanoparticles. The resolution was three to six times worse than with bright-field imaging. Measurements of nanometer-size objects showed a similar feature size in DF-FSED imaging despite a signal-to-noise ratio 12 times smaller. Monte Carlo simulations were used to predict the variation of the contrast of a CNT/Fe/Pt system as a function of the collection angles. It was constant for very high collection angles (>450 mrad) and confirmed experimentally. The reverse contrast between carbon black particles and the smallest titanium dioxide (TiO2) nanoparticles was predicted by Monte Carlo simulations and observed in the DF-FSED micrograph of a battery electrode coating. However, segmentation of the micrograph was not able to isolate the TiO2 nanoparticle phase because of the close contrast of small TiO2 nanoparticles compared to the C black particles.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".