Exploring the potential for scanning electron microscopy/focused ion beam - based diffraction for screening cryo-transmission electron microscopy samples
Bibliographic record
Abstract
Abstract The study of biological and organic materials at high resolution using cryogenic transmission-electron microscopy (cryo-TEM) necessitates vitrification to preserve the native structure. Assessing sample integrity is essential, particularly as ice crystallization during freezing and handling can cause irrecoverable structural damage. Usually, a secondary cryo-TEM is used for initial screening, only possible after a time-consuming sample preparation workflow. In the present work, we propose simple methods that exploit existing workflows developed for materials science analyses and demonstrate on-grid in situ assessment of ice crystallinity with electron backscatter diffraction (EBSD) on a direct electron detector (DED) in a cryo-scanning-electron microscope (SEM). This evaluation step can be performed prior to sample preparation for cryo-TEM by using cryogenic focused ion beam (cryo-FIB) milling. Custom grid holders and jigs were developed to integrate the clipped cryo-TEM grids and evolve the sample preparation workflow. EBSD detects hexagonal ice in some areas of the samples, whereas other areas show an absence of EBSD signal, consistent with vitreous ice, that enable targeting the further steps of sample preparation for cryo-TEM. Off-axis transmission Kikuchi diffraction (TKD) was attempted, but led to severe damage to polished TEM-lamellae and appears unsuitable. A proof-of-concept lift-out from a clipped cryo-TEM grid mounted on a support is introduced, demonstrating possibilities for expanded cryogenic correlative workflows beyond the acceleration of sample screening for cryo-TEM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".