Exploring Biomineralization Processes Using In Situ Liquid Transmission Electron Microscopy: A Review
Bibliographic record
Abstract
Liquid transmission electron microscopy (TEM) is a newly established technique broadly used to study reactions in situ. Since its emergence, complex and multifaceted biomineralization processes have been revealed with real-time resolution, where classical and non-classical mineralization pathways have been dynamically observed primarily for Ca and Fe-based mineral systems in situ. For years, classical crystallization pathways have dominated theories on biomineralization progression despite observations of non-traditional routes involving precursor phases using traditional- and cryo-TEM. The new dynamic lens provided by liquid TEM is a key correlate to techniques limited to time-stamped, static observations - helping shift paradigms in biomineralization toward non-classical theories with dynamic mechanistic visualization. Liquid TEM provides new insights into fundamental biomineralization processes and essential physiological and pathological processes for a wide range of organisms. This review critically reviews a summary of recent in situ liquid TEM research related to the biomineralization field. Key liquid TEM preparation and imaging parameters are provided as a foundation for researchers while technical challenges are discussed. In future, the expansion of liquid TEM research in the biomineralization field will lead to transformative discoveries, providing complementary dynamic insights into biological systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".