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Behavior of Magnesium in Hank's Solution Aimed to Trabecular Pattern of Natural Bone

2003· article· en· W2063265558 on OpenAlexaff
Hideyuki Kuwahara, Naoko Mazaki, Mamoru Mabuchi, C. Wein, Tatsuhiko Aizawa

Bibliographic record

VenueMaterials science forum · 2003
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMagnesiumMicrostructureMaterials scienceMineralogyArtificial boneMetallurgyChemical engineeringComposite materialChemistry

Abstract

fetched live from OpenAlex

An ideal artificial bone is expected to grow together with other natural bones with aid of osteoblast cells and to fade out into other natural bones at the same rate of restructuring natural bone. Magnesium is thought to be one of candidate materials, since it has a potential to enhance natural bone growth and to homogenize the implanted artificial bodies with natural bone. In the present study, we are concerned with the formation of trabecular pattern in the natural bone to consider how to reconstruct this pattern in the artificial bone made from magnesium. For that purpose, a series of experiments were performed to observe the chemical behavior of dipped magnesium plate and cellular magnesium in Hank's solution. A magnesium specimen is annealed at 773 - 803 K for various periods in an atmosphere to homogenize its microstructure. Mass change of magnesium is estimated by immersing it in Hank's solution. It is well known that magnesium is easily corroded by chlorine ion. Both x-ray diffraction and energy dispersed x-ray analyses were carried out in order to identify a reaction product and its chemical composition. Mass of a magnesium specimen, which was annealed at 803 K for 32.4 ks or 14.4 ks, increases after immersing it into Hank's solution for 4.518 Ms (1255 h). Furthermore, the cellular magnesium, which was annealed at 803 K for 1.8 ks, formed a reacted layer with around 80 μm in thickness and it contained Mg, Ca, P, and a little bit of Cl.

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 score0.008
Threshold uncertainty score0.651

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations15
Published2003
Admission routes1
Has abstractyes

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