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Record W2077588424 · doi:10.1680/emr.13.00019

Influence of coating bath chemistry on biomimetic calcium phosphate coatings

2013· article· en· W2077588424 on OpenAlexaff
Dan-Yi Yang, J.E. Gray-Munro

Bibliographic record

VenueEmerging Materials Research · 2013
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsLaurentian University
Fundersnot available
KeywordsBiocompatibilityMaterials scienceDissolutionMagnesiumCoatingCorrosionScanning electron microscopeChemical engineeringX-ray photoelectron spectroscopyFourier transform infrared spectroscopyMagnesium alloyConversion coatingCalciumPhosphateEnergy-dispersive X-ray spectroscopyMagnesium phosphateAttenuated total reflectionMetallurgyNanotechnologyComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Magnesium alloys are an emerging class of biodegradable medical implant materials. However, control of their degradation rate and biocompatibility are key issues that need to be addressed prior to their use in clinical applications. In this article, a two-step method to biomimetically deposit corrosion-resistant calcium phosphate coatings on magnesium alloy AZ31 is described. A decrease in the overall dissolution of the magnesium substrate of up to 99·8% was observed for the coated samples in comparison with non-treated magnesium AZ31. Surface characterization of the coatings was performed by attenuated total reflectance–Fourier transform infrared spectroscopy, scanning electron microscopy, energy-dispersive spectroscopy and X-ray photoelectron spectroscopy. The results indicate that the composition, morphology and corrosion resistance of the biomimetic calcium phosphate coatings depend strongly on the coating bath composition.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.047
GPT teacher head0.334
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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