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Metal Oxide Porous Coatings for Implantant Materials

2015· article· en· W2180352611 on OpenAlexvenueno aff
Leonid Skatkov, L.V. Lyashok, Valeriy Gomozov, I. A. Tokareva, Igor Kolupaev, Boris Bayrachniy

Bibliographic record

VenueJournal of Coating Science and Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials sciencePassivationOxideNiobiumTitaniumCorrosionCoatingMetallurgyAnodizingNiobium oxideNucleationAlloyLayer (electronics)Composite materialChemistry

Abstract

fetched live from OpenAlex

The paper presents the results of survey, dedicated to synthesis of metal oxide coatings on porous materials applied for implant surgery (stainless steel 12X18H9T, titanium alloy and high-purity niobium VT5). This article examines kinetic features of electrochemical formation of anodic oxide coatings on steel, niobium and titanium. It is shown that for steel anodic treatment method does not provide a reliable surface passivation (no current decay, the surface indicates the transition to the passive state).Analysis of polarization dependences obtained on niobium and titanium in electrolytes with an activator (F-), indicates surface passivation (current slump), and at potentials above 2 on the surface of a transition curves in transpassive state (as evidenced by the current increase). Consequently, it can be concluded that presence of F- results surface activation of titanium and niobium electrode (F- oxide reacts to form water-soluble complexes) that promotes nucleation and formation of pores of the porous structure of the oxide coating.SEM results verify the presence of self-organized porous oxide film synthesized on titanium and niobium in solutions containing F-.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.014
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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