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Record W7006155755

Study of the structural properties and control of degradation rate for biodegradable metallic stents using cold spray

2015· dissertation· en· W7006155755 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsMcGill University
Fundersnot available
KeywordsIndentation hardnessGas dynamic cold sprayGalvanic cellSubstrate (aquarium)MetalDeposition (geology)Shear strength (soil)Adhesive
DOInot available

Abstract

fetched live from OpenAlex

AbstractThere is considerable interest in fabricating stents with degradable materials to avoid the disadvantages of permanent stents such as stent fracture. The focus of this thesis is on forming degradable materials consisting of mixtures of two metal powders and determining their rate of galvanic corrosion. To fabricate degradable coatings suitable for producing a stent, the cold gas dynamic spray technique is used to combine the two powders into a single low-porosity layer. Stainless steel (316L SS) and commercial purity iron (CP Fe) powders are mixed together and sprayed onto a metallic substrate in order to produce an amalgamate material. In terms of cold sprayability, spraying the single component 316L powder leads to a higher deposition efficiency (DE) as compared to CP Fe powder (72% vs. 33%), but the porosities remain low (about 1%). It is observed that spraying a mixture of 20wt% CP Fe and 80wt% 316L results in a DE of 43%. Increasing the percentage of CP Fe in the mixture to 50% does not change the DE significantly, although a further increase to 80% results in a DE of 66%. The porosities of the mixed coatings remain low in all cases. These observations may be related to differences in the hardness of the 316L and CP Fe powders. As-sprayed mixed coatings exhibit microhardness values between the range of microhardness of 316L and CP Fe coatings. Also, the effect of mixing does not result in any decrease in the shear strength of as-sprayed coatings as compared to the shear strength of as-sprayed 316L and CP Fe coatings. It is found that annealing the coating relieves work hardening by recrystallization, reduces porosity, and promotes sintering. Shear punch test results indicate that annealed mixed coatings attain shear strengths of that of 316L (455 MPa) with approximately 15% decrease in the reduction in area. Reduction of the work hardening in both 316L and CP Fe particles increases the coating ductility. The 20wt% Fe coatings exhibit 23% ductility, which is sufficient to be used for a degradable stent. Immersion and potentiodynamic polarization tests of the as-sprayed coatings indicate no significant difference in the corrosion rates of 100wt%Fe, 80wt%Fe and 50wt%Fe, which indicates that the corrosion rate of iron is increasing with increasing 316L in the composite material, assuming that iron is the only component which is corroding. This indicates that galvanic corrosion is accelerating the corrosion rate of iron in the mixed coatings. After heat treatment, the corrosion rate generally decreases primarily due to the reduction of pores, which reduces the surface area that is responsible for the higher corrosion rate of the as-sprayed coatings. X-ray diffraction (XRD) tests show that iron oxide forms after degradation. Energy dispersive X-Ray spectroscopy (EDS) also indicates the formation of iron oxides on the mixed coatings which reduces the corrosion rate with time. Considerable pitting is observed on the iron particles after the degradation tests, which indicates that chlorides are pitting the surface of mixed coatings. Overall, the use of the cold spray technique for forming an amalgamate material subject to galvanic corrosion appears to be a potential method for the fabrication of degradable stent. Further clinical investigations are required to validate the performance of such stents.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.006
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.253
Teacher spread0.210 · 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.

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
Published2015
Admission routes1
Has abstractyes

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