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Record W4411085315 · doi:10.1016/j.jma.2025.05.006

Effect of nanostructured MgO directly grown on pure magnesium substrate on its in vitro corrosion and bioactivity behaviour

2025· article· en· W4411085315 on OpenAlexaff
Majid Shahsanaei, Masoud Atapour, M. Shamanian, Nastaran Farahbakhsh, Swathi Naidu Vakamulla Raghu, Torsten Kowald, Sybille Krauß, Seyedsina Hejazi, Shiva Mohajernia, Manuela S. Killian

Bibliographic record

VenueJournal of Magnesium and Alloys · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Alberta
FundersIsfahan University of TechnologyUniversität SiegenEuropean CommissionMinnesota Nurses Association Foundation
KeywordsMaterials scienceCorrosionMagnesiumSubstrate (aquarium)In vitroMetallurgyChemical engineeringNanotechnologyBiochemistryChemistry

Abstract

fetched live from OpenAlex

This study introduces a nanostructured MgO coating fabricated via anodization in a non-aqueous electrolyte, offering a novel approach to addressing the challenges of corrosion resistance and biofunctionality. The surface was characterized before and after immersion testing using field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). Electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization tests demonstrated a 2-fold reduction in the corrosion resistance compared to untreated magnesium. Biomineralization studies demonstrated the uniform formation of apatite with a Ca/P ratio of 1.35 on the nanostructured surface after 14 days in simulated body fluid (SBF), surpassing that of microstructured MgO. Hydrogen evolution decreased from 912±38 µL cm -2 for untreated Mg to 615±32 µL cm -2 for the Mg/MgO nanostructure and 545±29 µL cm -2 for the Mg/MgO/HA sample. These findings highlight the potential of nanostructured MgO coatings to advance Mg-based implants by providing enhanced corrosion protection, improved biomineralization, reduced hemolysis and increased cell viability, and reduced H 2 generation.

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 score0.061
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.245
Teacher spread0.237 · 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

Citations11
Published2025
Admission routes1
Has abstractyes

Explore more

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