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Record W4415862378 · doi:10.3390/prosthesis7060141

Electrochemical Evaluation of Ag–CaP–ZrO2 Composite Coatings on Ti6Al4V for Enhanced Corrosion Resistance in Dental Implants

2025· article· en· W4415862378 on OpenAlexafffund
Mohamed Aissi, Azzedine Er-Ramly, Nadia Merzouk

Bibliographic record

VenueProsthesis · 2025
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsCorrosionTitanium alloyCoatingOsseointegrationTitaniumComposite numberElectrochemistrySurface modification

Abstract

fetched live from OpenAlex

Objective: The Ti6Al4V titanium alloy is widely used for dental implants because of its excellent mechanical properties, corrosion resistance, and biocompatibility. However, its bioinert surface limits both osseointegration and resistance to bacterial colonization. Methods: To address these challenges, this study develops a composite coating based on calcium phosphate (CaP) and silver (Ag), reinforced with zirconium oxide (ZrO2). The coating was deposited on Ti6Al4V using an immersion technique to improve the surface properties of the alloy. Electrochemical analyses (OCP, EIS, and potentiodynamic polarization) were performed in simulated physiological conditions to evaluate the corrosion behavior, while SEM/EDS was used to characterize the surface morphology and composition. Results: The Ag- and Zr-containing CaP coatings significantly improved the corrosion resistance of Ti6Al4V compared with uncoated and CaP-coated samples. Conclusions: This approach provides a promising strategy to enhance the electrochemical stability and long-term durability of titanium dental implants, thereby contributing to their reliable performance in the oral environment.

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 score0.007
Threshold uncertainty score0.603

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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