MétaCan
Menu
Back to cohort
Record W4404082717 · doi:10.1016/j.mtla.2024.102285

Effects of plasma surface modification of Mg-2Y-2Zn-1Mn for biomedical applications

2024· article· en· W4404082717 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterialia · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsPlasmionique (Canada)McGill UniversityUniversité Laval
FundersUniversité Laval
KeywordsMaterials scienceSurface modificationPlasmaNanotechnologyChemical engineering

Abstract

fetched live from OpenAlex

Magnesium (Mg) alloys have emerged as promising materials for biodegradable implants in orthopedic, oral, and cardiovascular applications. Despite their potential, high corrosion rate, and release of diatomic hydrogen in the surrounding environment remain the unmet challenges. In this research, oxygen plasma ion immersion implantation (O-PIII) was investigated in an attempt to modify the degradation rate of Mg-2Y-2Zn-1Mn alloy. In particular, the effects of pulse duration ( t pd ) and pressure ( p ) on the degradation rate were investigated. For all the investigated conditions, plasma treatment enriched the surface chemical composition with O, forming a Mg- and Y- rich oxide layer. Mg and Y elements were mainly concentrated at grain boundaries. The concurrent phenomena of sputtering and energetic implantation led to crystalline Y 2 O 3 formation. Electrochemical investigations confirm that the degradation rate of samples decreased significantly, from ∼0.23 mm/y for untreated to ∼0.07 mm/y for O-PIII conditions. These findings demonstrate the effectiveness of O-PIII in changing surface properties and controlling corrosion rate of Mg alloys.

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.

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)0.000
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 score0.066
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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