Coupled Broad Ion Beam–Scanning Electron Microscopy (BIB–SEM) for polishing and three dimensional (3D) serial section tomography (SST)
Bibliographic record
Abstract
plasma-FIB (PFIB) methods inducing less beam damage, especially for ion beam sensitive materials. It can mill areas several orders of magnitude larger (up to millimetre scale), and is not confined to the edge of the sample with associated curtaining issues. BIB is shown to have sputter rates up to five times higher than comparable FIB techniques. This new coupled BIB-SEM system (commercial name 'iPrep™II') enables in-microscope surface polishing to remove contaminants or damage for two dimensional (2D) imaging, as well as automated serial section tomography (SST) by milling and imaging hundreds of slices, cost and time efficiently. The milled slice thickness can be controlled from a few nanometers up to a micrometre. A novel sample transfer, handling and interlock system allows automated and sequential BIB polishing, scanning electron microscopy (SEM) and analysis by secondary electron (SE) imaging, electron back scatter diffraction (EBSD) and energy dispersive spectroscopy (EDS) for 3D microstructure analysis. Furthermore, insulating surfaces can be sputter coated after milling each slice to reduce charging during SEM analysis. The performance of the instrument is demonstrated through a series of case studies across the materials, earth and life sciences exploiting the imaging, crystallographic and chemical mapping capabilities. These include the study of butterfly defects in bearing steels, meta-stable intermetallic phases in bronze bearings, shale gas rock, aluminium plasma electrolytic oxide (PEO) coatings as well as liver and mouse brain tissues.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".