FIB Specimen Preparation for Liquid-TEM: A Case Study on Interfacial Mineralization
Bibliographic record
Abstract
Abstract Innovations in the liquid-transmission electron microscopy (TEM) field are leading to exciting new possibilities to explore nanoscale dynamic events. Within biological fields, researchers studying biomineralization have leveraged liquid-TEM to probe biomimetic reactions unfolding in real time and to study hydrated samples. However, limited research has been conducted studying interfacial mineralization using this technique. Interfacial mineralization is critical for understanding inorganic-organic interactions, relevant to many biomineralization events, and for studying biomaterials and their interaction with hard tissues. A detailed method for exploring biomaterial interfacial mineralization using nanofabrication with a focused ion beam for in situ liquid-TEM preparation is shared herein. Calcium phosphate (CaP) and titanium (Ti) interfacial mineralization events were observed, where the nucleation and evolution of different CaP particles were noted to form assemblies onto and adjacent to Ti lamellae in situ. With this complementary new method to explore interfacial mineralization mechanisms, this work trailblazes new avenues to study biomineralization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".