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Record W2518836726 · doi:10.1111/ijac.12589

Mechanical Properties of Plasma‐Sprayed Mullite‐Reinforced Titania–Bioglass Composite

2016· article· en· W2518836726 on OpenAlexaff
Suzan Bsat, Xiao Huang

Bibliographic record

VenueInternational Journal of Applied Ceramic Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceMulliteComposite materialMicrostructureFracture toughnessBrittlenessToughnessComposite numberCoatingFiberScanning electron microscopeIndentationCeramic

Abstract

fetched live from OpenAlex

Bioceramics have been extensively used for various medical applications including hip and knee prostheses, tissue engineering scaffolds, and dental implants. Bioceramics, particularly bioglass, are desired because of their bioactivity but are often limited by their inherent brittleness. To compensate, composites have been formed to obtain unique properties where both bioactivity and mechanical integrity can be achieved. Mullite‐reinforced titania–bioglass (TiO 2 – BG ) composites were therefore deposited using plasma spraying technique. The microstructure of the coating materials were analyzed for their morphology and microstructure using scanning electron microscopy/energy dispersive spectrometry. Mechanical properties of the coatings were tested using three‐point bend test, indentation test, and pin‐on‐disk wear test to determine their fracture strength, fracture toughness, and wear resistance, respectively. The addition of mullite fibers improved the fracture strength and wear resistance of TiO 2 – BG composites while having minimal effect on fracture toughness. After the addition of mullite, failure mode was bimodal, failing intergranularly and by fiber pull‐out. Although mullite fibers have not been particularly used for medical applications, fiber reinforcement has shown efficacy in mechanically reinforcing composites of various medical applications.

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 score0.024
Threshold uncertainty score0.492

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 science0.0010.000
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 head0.195
Teacher spread0.189 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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