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Record W3025816795 · doi:10.1149/ma2020-0114988mtgabs

Assessing the Corrosion Pattern of Magnesium Alloy Implants in Simulated Salivary Fluids at Different pH

2020· article· en· W3025816795 on OpenAlexaff
Ubong Eduok

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCorrosionPassivationMaterials scienceDielectric spectroscopyMagnesiumPolarization (electrochemistry)AlloyScanning electron microscopeOxideMetallurgyChemical engineeringElectrochemistryComposite materialChemistryElectrode

Abstract

fetched live from OpenAlex

Magnesium (Mg) alloy implants are formidable structural metals for biomedical applications, they are limited due to Mg chemical reactivity and its susceptible to corrosion. Efficient mitigation control toward Mg corrosion can only be proposed if its degradation kinetics are properly understood in various media. In the biomedical field, the auto-controlled degradation mechanism of orthopedic Mg implants eliminates repeated surgeries while also minimizing the risks associated with these medical procedures. However, their rapid degradation kinetics between living tissues contribute to sudden changes in local pH value, accumulation of unwanted hydrogen gas in bone cavities and subsequent mechanical failure. In this work, we have investigated the degradation pattern of Mg alloy implant in chloride-enriched simulated salivary fluids at different pH at room temperature. The implant demonstrated varying rates of corrosion and passivation within a range of pH of corrosion media. The effects of solution treatment on implant corrosion behavior was vividly studied and the trend of increasing corrosion was low pH > saline pH > high pH. Localized corrosion behavior has been observed by scanning electron microscopy evidence while electrochemical impedance spectroscopy and potentiodynamic polarization techniques revealed increased anodic polarization and charge transfer, respectively. The chemistry and diffraction phases of adhering corrosion products as well as their surface morphologies was also been identified. The implant must have uniformly corroded within the salivary fluids over an extended duration; however, the early corrosion stages reveal localized passivation of oxide films at grain boundaries at low pH. Most metallic implants made of corrosion-resistant alloys (e.g., cobalt, titanium and nickel) are excellent candidates for oral surgeries. However, most of them cannot serve as bone cements to stabilize dental implants during post-surgery tissue healing; a function that is possible with magnesium-based bone cement [1,2]. The use of these metal-type temporary implants in binding bone structures normally will require a secondary surgery operation to remove them, and this might lead to potential infections. This study features the analyses of the corrosion patterns of Mg alloy implants deployed in orthopedic applications. The salivary pH changes induce corrosion toward septic loosening prior to Mg implant degradation and dental implant infections. References [1] A. Mohamed, A.M. El-Aziz, H.G. Breitinger, Journal of Magnesium and Alloys, 7, 249 (2019). [2] B.M. Sehlke, T.G. Wilson, A.A. Jones, M. Yamashita, D.L. Cochran, Journal of the Academy of Osseointegration, 28, 357 (2013).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.034
GPT teacher head0.271
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

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