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Record W1992076133 · doi:10.1016/j.jascer.2014.04.002

In vitro bioactivity of 3D Ti-mesh with bioceramic coatings in simulated body fluid

2014· article· en· W1992076133 on OpenAlexaff
Wei Yi, Xudong Sun, Dun Niu, Xiaozhi Hu

Bibliographic record

VenueJournal of Asian Ceramic Societies · 2014
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsScience North
FundersNational Natural Science Foundation of ChinaChina Scholarship CouncilUniversity of Western Australia
KeywordsSimulated body fluidMaterials scienceApatiteBioceramicComposite numberComposite materialCoatingBioactive glassSubstrate (aquarium)Layer (electronics)CeramicTape castingChemical engineeringMicrostructurePorosityScanning electron microscope

Abstract

fetched live from OpenAlex

3D Ti-mesh has been coated with bioceramics under different coating conditions, such as material compositions and micro-porosity, using a dip casting method. Hydroxyapatite (HA), micro-HA particles (HAp), a bioglass (BG) and their different mixtures together with polymer additives were used to control HA-coating microstructures. Layered composites with the following coating-to-substrate designs, such as BG/Ti, HA + BG/BG/Ti and HAp + BG/BG/Ti, were fabricated. The bioactivity of these coated composites and the uncoated Ti-mesh substrate was then investigated in a simulated body fluid (SBF). The Ti-mesh substrate and BG/Ti composite did not induce biomimetic apatite deposition when they were immersed in SBF for the selected BG, a pressable dental ceramic, used in this study. After seven days in SBF, an apatite layer was formed on both HA + BG/BG/Ti and HAp + BG/BG/Ti composites. The difference is the apatite layer on the HAp + BG/BG/Ti composite was rougher and contained more micro-pores, while the apatite layer on the HA + BG/BG/Ti composite was dense and smooth. The formation of biomimetic apatite, being more bioresorbable, is favored for bone regeneration.

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)0.001
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 score0.028
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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