Liquid-cell annular dark-field scanning transmission electron microscopy imaging of single crystal samples on a low-index zone-axis incidence condition
Bibliographic record
Abstract
In liquid cell (LC) annular dark-field scanning transmission electron microscopy (ADF-STEM), the spatial resolution is limited by the low ratio of signals from samples to background signals from the silicon nitride window membranes and liquid. We report the development of a double-tilt LC holder for atomic-resolution LC-ADF-STEM imaging of single-crystal samples under zone-axis incidence conditions. A SrTiO 3 <001> lamellar sample, approximately 100 nm thick, was prepared using the focused ion beam technique and transferred onto a silicon nitride window membrane of an LC chip via a glass probe pick-up method in air, which avoids Ga ion beam-induced damage to the window membrane. The sample adhered to the high-flatness window membrane and remained immobile, even when embedded in a water droplet. The sample and pure water were enclosed in an LC and observed under <001> zone-axis incidence conditions using aberration-corrected ADF-STEM. Electron channeling along the atomic columns enabled atomic-resolution LC-ADF-STEM imaging with high contrast, sufficiently overcoming background signals from window membranes and liquids. This high-contrast imaging technique could lower the probe current and is expected to mitigate electron-beam-induced radiolysis and minimize undesired sample damage, particularly under high-magnification imaging conditions. • A double-tilt LC holder was developed for atomic-resolution LC-ADF-STEM imaging under zone-axis incidence conditions. • FIB-sample transfer using a glass probe pick-up method can avoid Ga ion beam-induced damage to liquid cell window membranes. • FIB samples adhere to high-flatness liquid cell window membranes and are stable even when embedded in a water droplet. • Electron channeling enables the high-contrast ADF-STEM imaging of atomic columns of sample in a liquid cell.
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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 | 0.000 | 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) | 0.002 | 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".