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Record W2804570256 · doi:10.5006/2674

Nano-Thick Amorphous Oxide Layer Produced by Plasma on Type 316L Stainless Steel for Improved Corrosion Resistance Under Plastic Deformation

2018· article· en· W2804570256 on OpenAlexafffund
Megan Mahrokh Dorri, Stéphane Turgeon, Maxime Cloutier, Pascale Chevallier, Diego Mantovani

Bibliographic record

VenueCORROSION · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversité LavalHôpital Saint-François d'Assise
FundersNatural Sciences and Engineering Research Council of CanadaCentre Hospitalier Universitaire de Québec
KeywordsMaterials scienceCorrosionOxideAmorphous solidDielectric spectroscopyMetallurgyLayer (electronics)Deformation (meteorology)MicrostructureComposite materialElectrochemistryElectrode

Abstract

fetched live from OpenAlex

Localized corrosion constitutes a major concern in medical devices made of stainless steel. The conventional approach to circumvent such a problem is to convert the surface polycrystalline microstructure of the native oxide layer to an amorphous oxide layer, a few micrometers thick. This process cannot, however, be used for devices such as stents that undergo plastic deformation during their implantation, especially those used in vascular surgery for the treatment of cardiac, neurological, and peripheral vessels. This work explores the feasibility of producing a nano-thick plastic-deformation resistant amorphous oxide layer by plasma-based surface modifications. By varying the plasma process parameters, oxide layers with different features were produced and their properties were investigated before and after clinically-relevant plastic deformation. These properties and the related corrosion mechanisms were mainly evaluated using the electrochemical methods of open-circuit potential, cyclic potentiodynamic polarization, and electrochemical impedance spectroscopy. Results showed that, under optimal conditions, the resistance to corrosion and to the permeation of ions in a phosphate buffered saline, even after deformation, was significantly enhanced.

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 score0.147
Threshold uncertainty score0.980

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 head0.018
GPT teacher head0.223
Teacher spread0.206 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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Same venueCORROSIONSame topicMetal and Thin Film MechanicsFrench-language works237,207