Workflow automation of SEM acquisitions and feature tracking
Bibliographic record
Abstract
Acquiring multiple high magnification, high resolution images with scanning electron microscopes (SEMs) for quantitative analysis is a time consuming and repetitive task for microscopists. We propose a workflow to automate SEM image acquisition and demonstrate its use in the context of nanoparticle (NP) analysis. Acquiring multiple images of this type of specimen is necessary to obtain a complete and proper characterization of the NP population and obtain statistically representative results. Indeed, a single high magnification image only scans a small area of sample, containing only few NPs. The proposed workflow is successfully applied to obtain size distributions from image montages at three different magnifications (20,000x, 60,000x and 200,000x) on the same area of the sample using a Python based script. The automated workflow consists of sequential repositioning of the electron beam, stitching of adjacent images, feature segmentation, and NP size computation. Results show that NPs are best characterized at higher magnifications, since lower magnifications are limited by their pixel size. Increased accuracy of feature characterization at high magnification highlights the importance of automation: many high-magnification acquisitions are required to cover a similar area of the sample at low magnification. Therefore, we also present feature tracking with smart beam positioning as an alternative to blind acquisition of very large image arrays. Feature tracking is achieved by integrating microscope tasks with image processing tasks, and only areas of interest will be imaged at high resolution, reducing total acquisition duration.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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".