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Surface functionalization of anodized tantalum with Mn3O4 nanoparticles for effective corrosion protection in simulated inflammatory condition

2021· article· en· W3205162782 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

VenueCeramics International · 2021
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersHorizon 2020European Commission
KeywordsMaterials scienceTantalumAnodizingCorrosionDielectric spectroscopySurface modificationCoatingEthylene glycolAmmonium fluorideAnodeNanoparticleElectrolyteTitaniumChemical engineeringElectrochemistryInorganic chemistryMetallurgyAluminiumComposite materialNanotechnologyChemistry

Abstract

fetched live from OpenAlex

The study highlights the corrosion behavior of untreated and treated tantalum with addition of trimanganese tetraoxide (Mn3O4) nanoparticles in simulated inflammatory media. The anodic layer was produced on pure tantalum by anodization in electrolytes composed of ammonium fluoride, ethylene glycol, and water. Nanoparticles were deposited uniformly on the surface of the anodized tantalum with the electrophoretic deposition (EPD) method. The results revealed that the anodic/EPD coating possessed more compact microstructure and higher bond strength than the anodic coating. Simulated inflammatory medium was based on phosphate-buffered saline with additions of H2O2 and HCl. Potentiodynamic polarization and electrochemical impedance spectroscopy studies showed that the anodic and Mn3O4 layers protected the tantalum from corroding in an acidic inflammatory condition. Finally, the corrosion protection mechanism of Mn3O4 NPs in inflammatory condition was presented.

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 score0.119
Threshold uncertainty score0.466

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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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