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Record W4414299619 · doi:10.1016/j.bbiosy.2025.100123

Development of biodegradable Fe-Mn thin structures by electroforming in deep eutectic solvents

2025· article· en· W4414299619 on OpenAlexafffund
Vinicius Sales de Oliveira, Carlo Paternoster, Francesco Copes, P. Mengucci, Gabriele Grima, Marcello Cabibbo, Γεώργιος Κολλιόπουλος, Diego Mantovani

Bibliographic record

VenueBiomaterials and Biosystems · 2025
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesFonds de recherche du QuébecNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsElectroformingEutectic systemCorrosionCastingThin filmNanoparticle

Abstract

fetched live from OpenAlex

Fe-Mn alloys represent promising candidates for temporary biomedical intravascular implants with a thin structure (e.g., coronary, cerebral and peripheral stents) due to their high mechanical strength, acceptable biocompatibility, and controllable corrosion rate. Traditionally, these devices are produced by casting followed by thermo-mechanical processing, i.e. a time- and energy-intensive top-to-bottom approach. This study explores electroforming as an alternative method to fabricate bottom-to-top thin Fe-Mn structures using ethylene glycol-based deep eutectic solvents (DESs). Glycine was introduced as a complexing agent to enhance Mn co-deposition. Electroforming was investigated in presence of three glycine concentrations (0.2, 0.4, and 0.6 M), and the the microstructure, composition, corrosion behavior, and cytocompatibility of the developed thin (50-85 µm) structures were characterized. Higher glycine content improved Mn incorporation, crystallinity, hardness and increased corrosion rate. These findings support the use of DES-based electroforming as a promising route for fabricating biodegradable Fe-Mn devices with tunable properties.

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)0.000
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 score0.003
Threshold uncertainty score0.540

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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