MétaCan
Menu
Back to cohort
Record W1975578424 · doi:10.1179/174329008x284868

Iron–manganese: new class of metallic degradable biomaterials prepared by powder metallurgy

2008· article· en· W1975578424 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.

Bibliographic record

VenuePowder Metallurgy · 2008
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceAlloyPowder metallurgyCorrosionMetallurgyMicrostructureManganeseMetalBiomaterialAusteniteComposite materialNanotechnology

Abstract

fetched live from OpenAlex

An Fe–35 wt-%Mn alloy, aimed to be used as a metallic degradable biomaterial for stent applications, was prepared via a powder metallurgy route. The effects of processing conditions on the microstructure, mechanical properties, magnetic susceptibility and corrosion behaviour were investigated and the results were compared to those of the SS316L alloy, a gold standard for stent applications. The Fe35Mn alloy was found to be essentially austenitic with fine MnO particles aligned along the rolling direction. The alloy is ductile with a strength approaching that of wrought SS316L. It exhibits antiferromagnetic behaviour and its magnetic susceptibility is not altered by plastic deformation, providing an excellent MRI compatibility. Its corrosion rate was evaluated in a modified Hank's solution, and found superior to that of pure iron (slow in vivo degradation rate). In conclusion, the mechanical, magnetic and corrosion characteristics of the Fe35Mn alloy are considered suitable for further development of a new class of degradable metallic biomaterials.

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)0.001
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 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 score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.001

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 head0.027
GPT teacher head0.238
Teacher spread0.211 · 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