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Record W4221083754 · doi:10.1002/adem.202101496

Ultrashort Laser Texturing for Tuning Surface Morphology and Degradation Behavior of the Biodegradable Fe–20Mn Alloy for Temporary Implants

2022· article· en· W4221083754 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

VenueAdvanced Engineering Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversité Laval
FundersConsiglio Nazionale delle Ricerche
KeywordsMaterials scienceAlloyDegradation (telecommunications)Morphology (biology)Surface modificationLaserComposite materialMetallurgyNanotechnologyOpticsChemical engineering

Abstract

fetched live from OpenAlex

Biodegradable metallic alloys are promising materials for the development of temporary medical implants, including cardiovascular and orthopedic devices. The present work explores the surface modification of a biodegradable Fe–20Mn alloy by means of ultrashort laser texturing and the correlation between the laser‐induced surface morphology and the degradation behavior. In detail, the effect of different processing parameters on surface morphology is explored and compared with the unprocessed surface. It is found that laser texturing can tune the degradation rate, selectively inducing positive (+29%) or negative (−31%) change with respect to the untreated surface.

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 score0.015
Threshold uncertainty score0.474

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 head0.228
Teacher spread0.213 · 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