Ab Initio Simulation of Si-Doped Hydroxyapatite
Bibliographic record
Abstract
Si-doped hydroxyapatite is a bioceramic useful as a bone repair material retaining the hexagonal structure of hydroxyapatite up to about 2 wt % Si. Different mechanisms for charge compensation for the SiO 4 4 - ion substituting for the PO 4 3 - have been proposed. Also, variations are reported in the dependence of the lattice parameters on the Si doping. These may be a result of different charge compensation mechanisms which may in turn depend on the method of preparation of the material. Calculation using ab initio total energy methods have been performed to investigate different mechanisms for charge compensation in Si-doped hydroxyapatite. Mechanisms involving an OH vacancy, an oxygen vacancy, and an additional hydrogen are studied. These mechanisms correspond to different degrees of dehydration and, consequently, depend on water partial pressure and chemical potential. Full relaxation of the atomic positions and the unit cell parameters was performed, and ground-state energies of the equilibrium structures, equilibrium lattice parameters, and atomic arrangements were obtained. The results indicate that which charge compensation mechanism is stable depends on the chemical potential of water. For small values of the water chemical potential the mechanism involving an OH vacancy is stable, but for larger values the mechanism leading to the formation of HSiO 4 is stable. The other mechanisms considered are unstable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".