Simulating STEM Imaging of Nanoparticles in Micrometers-Thick Substrates
Why this work is in the frame
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Bibliographic record
Abstract
Scanning transmission electron microscope (STEM) images of three-dimensional (3D) samples were simulated. The samples consisted of a micrometer(s)-thick substrate and gold nanoparticles at various vertical positions. The atomic number (Z) contrast as obtained via the annular dark-field detector was generated. The simulations were carried out using the Monte Carlo method in the CASINO software (freeware). The software was adapted to include the STEM imaging modality, including the noise characteristics of the electron source, the conical shape of the beam, and 3D scanning. Simulated STEM images of nanoparticles on a carbon substrate revealed the influence of the electron dose on the visibility of the nanoparticles. The 3D datasets obtained by simulating focal series showed the effect of beam broadening on the spatial resolution and on the signal-to-noise ratio. Monte Carlo simulations of STEM imaging of nanoparticles on a thick water layer were compared with experimental data by programming the exact sample geometry. The simulated image corresponded to the experimental image, and the signal-to-noise levels were similar. The Monte Carlo simulation strategy described here can be used to calculate STEM images of objects of an arbitrary geometry and amorphous sample composition. This information can then be used, for example, to optimize the microscope settings for imaging sessions where a low electron dose is crucial for the design of equipment, or for the analysis of the composition of a certain specimen.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it