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Record W2023871718 · doi:10.1021/cm051989x

Ab Initio Simulation of Si-Doped Hydroxyapatite

2005· article· ceb· W2023871718 on OpenAlexaff
R. Astala, Lázaro Calderín, Xinmao Yin, M. J. Stott

Bibliographic record

VenueChemistry of Materials · 2005
Typearticle
Languageceb
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsVacancy defectAb initioMaterials scienceDopingPartial chargeAb initio quantum chemistry methodsHydrogenIonChemical physicsChemistryCrystallographyMolecule

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.012
GPT teacher head0.229
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2005
Admission routes1
Has abstractyes

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