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Record W4294830148 · doi:10.31399/asm.cp.itsc2006p0017

Plasma Spraying of Hydroxyapatite Coatings Obtained from Two different Precursors

2006· article· en· W4294830148 on OpenAlexaff
E. Garcı́a, Z.B. Zhang, Thomas W. Coyle, Lu Gan, Robert M. Pilliar

Bibliographic record

VenueThermal spray · 2006
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanocrystalline materialMaterials scienceChemical engineeringSolution precursor plasma sprayCoatingTransmission electron microscopyCalcium nitrateAmmonium dihydrogen phosphateTetrahydratePorosityAmmonium phosphateThermal sprayingPhosphateMicrostructureMineralogyInorganic chemistryCalciumMetallurgyComposite materialNanotechnologyRaw materialChemistryOrganic chemistryCrystal structure

Abstract

fetched live from OpenAlex

Abstract In this work the suitability of two different liquid precursors of hydroxyapatite (HA) as feedstock for the air plasma spray (APS) coating technique was studied and compared. The precursors were organic (calcium nitrate tetrahydrate and triethyl phosphate) and inorganic (calcium nitrate tetrahydrate and ammonium dihydrogen phosphate) sol-gel solution precursors employed in a previous work to produce thin films by a dipping technique. We report on the formation of porous hydroxyapatite coatings on Ti6Al4V substrates. The microstructures found in of both types of coatings are characterized by features typically found in solution precursor plasma spray processes; a combination of melted and unmelted deposits and small hollow spheres. Transmission electron microscopy revealed submicron/nanocrystalline features forming those deposits. Hydroxyapatite was the main crystalline phase present in the coatings. Small amount of other crystalline phases were also found due to the high temperature of the substrates during the spraying.

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 score0.120
Threshold uncertainty score0.997

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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
Published2006
Admission routes1
Has abstractyes

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