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Record W4407224432 · doi:10.1002/jbm.b.35542

Effects of Nitrogen and Hydrogen Plasma Treatments on a Mg‐2Y‐1Zn‐1Mn Resorbable Alloy

2025· article· en· W4407224432 on OpenAlexafffund
Masoud Shekargoftar, Samira Ravanbakhsh, Vinicius Sales de Oliveira, Carlo Paternoster, Pascale Chevallier, Frank Witte, A. Sarkissian, Diego Mantovani

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsPlasmionique (Canada)Université Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAlloyWettingMaterials scienceHydrogenNitrogenCorrosionPlasmaContact angleOxideMetallurgyNuclear chemistryChemical engineeringChemistryComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Mg alloys have recently been investigated and optimized for the development of biodegradable implants for orthopedic, dental, vascular, and other applications. However, their rapid degradation in a physiological environment remains the main obstacle to their development. In this work, the effects of nitrogen and hydrogen plasma treatments on the surface properties and corrosion behavior of an Mg‐2Y‐1Zn‐1Mn (WZM211) alloy were investigated. Plasma treatment effectively modified the surface of a WZM211 alloy by removing the original oxide layer, followed by the formation of a new surface layer with controlled composition, thickness, and wettability. The water contact angle decreased from 100° to 17° after nitrogen plasma and to 45° after hydrogen plasma treatment. The nitrogen plasma treatment, shortly N‐Plasma, resulted in the lowest corrosion rate (CR N = 0.038 ± 0.010 mm/y) if compared with the hydrogen plasma treatment, shortly H‐Plasma (CR H = 0.044 ± 0.003 mm/y) and untreated samples (0.233 ± 0.097 mm/y). The results demonstrate the potential of nitrogen and hydrogen plasma treatment for the development of resorbable Mg‐based implants.

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)0.004
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.315
Teacher spread0.290 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part B Applied BiomaterialsSame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207