Real-Time Visualization of Calcium Phosphate Formation on Titanium Dioxide Nanoparticles Using Liquid Transmission Electron Microscopy
Bibliographic record
Abstract
Abstract The integration of titanium dioxide (TiO 2 ) with calcium phosphate (CaP)-based hydroxyapatite (HAP) is a promising strategy for enhancing the bioactivity of bone implants. However, a fundamental understanding of the interfacial reactions governing CaP mineralization on TiO 2 remains limited due to lack of characterization techniques with sufficient spatial and temporal resolution in hydrated state. In this study, we combined in situ liquid TEM imaging and correlative ex situ TEM analyses to investigate the nucleation, aggregation, and crystallization of the CaP layer on TiO 2 nanoparticle surfaces. Our findings reveal a three-step mineralization process: (1) initial aggregation of TiO 2 nanoparticles in solution, (2) formation of an amorphous calcium phosphate-like (ACP-like) layer on the TiO 2 surface, leading to an ACP-like coated TiO 2 nanoparticle structure, and (3) progressive crystallization of ACP into HAP, forming a HAP-like coated TiO 2 nanoparticle structure. Liquid-TEM imaging captured dynamic transformations, including nanoparticle aggregation, structural evolution, and phase transitions, providing unprecedented insights into the physicochemical interactions underlying mineralization. Additionally, we evaluated the effects of electron beam exposure on TiO 2 nanoparticles, demonstrating that high electron flux densities can induce morphological instability. This study advances our understanding of CaP-TiO 2 interfacial mineralization and offers valuable guidance for optimizing bioactive coatings to improve osseointegration and the long-term stability of bone implants.
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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) | 0.001 | 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".