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Spontaneous and Biomimetic Apatite Formation on Pure Magnesium

2007· article· en· W2082667262 on OpenAlexaff
Dora A. Cortés‐Hernández, Haydée Y. López, Diego Mantovani

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSimulated body fluidMagnesiumApatiteMaterials scienceImmersion (mathematics)WollastoniteHomogeneousMetalMetallurgyCeramicNuclear chemistryMineralogyChemical engineeringChemistry

Abstract

fetched live from OpenAlex

In order to decrease its degradation rate, pure magnesium was subjected to the following treatments: (1) heat treatment at 345oC for 15 min and (2) heat treatment at 380°C for 30 min followed by hot rolling at 350°C. The treated samples and non-treated controls were immersed in simulated body fluid (SBF) at 37oC for different periods of time. In all cases, the magnesium released into the SBF, the weight loss of the specimens and the pH of SBF increased with time of immersion. The hot-rolled samples showed a lower degradation rate and lower pH values. A lower increase of magnesium concentration in the SBF corresponding to the hot-rolled samples was also observed. The main and unexpected positive finding of this work was that in all cases, a layer of Ca, P-rich was formed on the substrates after only 3 days of immersion in SBF. This indicates that metallic magnesium is a potential bioactive material. In the aim to promote the formation of a thicker bioactive layer than the one observed on the samples immersed in single SBF, hot-rolled magnesium was biomimetically-treated using wollastonite ceramics, SBF and a more concentrated solution (1.5 SBF). A homogeneous and dense bone-like apatite layer was observed on the biomimetically-treated samples.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.005
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2007
Admission routes1
Has abstractyes

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