Biomineralization at Titanium Revealed by Correlative 4D Tomographic and Spectroscopic Methods
Bibliographic record
Abstract
Abstract At an implant biointerface, where an engineered material merges into a biological environment, complex biophysicochemical interactions occur. One typical biointerface is the bond between human bone and dental or orthopedic implants, which is based on the biomineralization of essential bone components such as hydroxyapatite, at the implant surface. However, the exact bonding mechanism between bone and implants is still unclear. The distribution of both the mineralized and organic components of bone at the interface, and their origins, requires improved characterization. Here, the first correlative characterization is reported using multiple‐length‐scale tomography and spectroscopy techniques to probe the chemical structure of the biointerface between human bone and commercial titanium dental implant down to the atomic scale in four dimensions (4D). The existence of an intervening transition zone bonding mature bone tissue is demonstrated to implant at multiple length scales, where the phase of bone mineral differs immediately adjacent to the implant and atomic‐scale osseointegration is confirmed. The correlative 4D electron energy loss spectroscopy tomography and atom probe tomography workflow established herein is transferable to other applications in materials or biological sciences.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".