Nanoscale imaging of Li and B in nuclear waste glass, a comparison of ToF‐SIMS, NanoSIMS, and APT
Bibliographic record
Abstract
It has been very difficult to use popular elemental imaging techniques to image Li and B distribution in glass samples with nanoscale resolution. In this study, time‐of‐flight secondary ion mass spectrometry, nanoscale secondary ion mass spectrometry, and atom probe tomography (APT) were used to image the distribution of Li and B in two representative glass samples, and their performance was comprehensively compared. APT can provide three‐dimensional Li and B imaging with very high spatial resolution (≤2 nm). In addition, absolute quantification of Li and B is possible, although there remains room for improving accuracy. However, the major drawbacks of APT include poor sample compatibility and limited field of view (normally ≤100 × 100 × 500 nm 3 ). Comparatively, time‐of‐flight secondary ion mass spectrometry and nanoscale secondary ion mass spectrometry are sample‐friendly with flexible field of view (up to 500 × 500 µm 2 and image stitching is feasible); however, lateral resolution is limited to only about 100 nm. Therefore, secondary ion mass spectrometry and APT can be regarded as complementary techniques for nanoscale imaging of Li and B in glass and other novel materials. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".