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Record W4295578626 · doi:10.1080/00202967.2022.2107771

Hydroxyapatite nanoparticles coating on Ti-6Al-4V substrate using plasma spray method

2022· article· en· W4295578626 on OpenAlexaff
P. Bagheri, Saeed Saber‐Samandari, Ali Sadeghi, Shiva Akhtarian, Ali Doostmohammadi

Bibliographic record

VenueTransactions of the IMF · 2022
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsYork University
Fundersnot available
KeywordsMaterials scienceCoatingCrystallinityDissolutionResidual stressAmorphous solidSubstrate (aquarium)TitaniumComposite materialAdhesionChemical engineeringAmorphous calcium phosphateAlloyCeramicNanoparticleTitanium alloyThermal sprayingMetallurgyPhase (matter)NanotechnologyCalciumChemistry

Abstract

fetched live from OpenAlex

In this research, hydroxyapatite powder particles were coated on a Ti-6Al-4V alloy substrate by the plasma spraying method to create bone implants by benefitting from titanium's mechanical properties and hydroxyapatite's biological properties. Hydroxyapatite prepared by the plasma spraying method suffers from decomposition, formation of other calcium phosphate phases, weak adhesion to the substrate, and microcrack formation in the coating due to residual stresses initiated by the high temperature of the coating process. To improve the hydroxyapatite coating properties, the Ti-6Al-4V alloy was preheated, and then hydroxyapatite coating took place. The results showed that residual stress in the interface decreased, and adhesion improved by preheating the substrate. However, forming an amorphous phase on coating was observed, resulting in a higher dissolution rate in the biological environment and weak mechanical properties compared to crystalline hydroxyapatite. Experimental results showed that heat treatment after the coating process decreased the amount of this amorphous phase and heightened the crystallinity of the coating by up to more than 60%.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.543

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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the IMFSame topicBone Tissue Engineering MaterialsFrench-language works237,207