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Record W2163499505 · doi:10.1243/09544119jeim513

Toughening of porous bioceramic scaffolds by bioresorbable polymeric coatings

2009· article· en· W2163499505 on OpenAlexaff
Sergey V. Dorozhkin, T. Ajaal

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2009
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsBioceramicTougheningMaterials sciencePorosityComposite materialToughness

Abstract

fetched live from OpenAlex

The mechanical properties of poly(c-caprolactone) (PCL)-coated porous bioceramic scaffolds made of calcium phosphates were studied and analysed using a statistical experimental design and Taguchi methods. In this study, both the flexural strength of the coated scaffolds and the amount of deposited PCL were considered as the measured responses. A statistical experimental design using the analysis of means and orthogonal array was applied to optimize these responses. The removal technique of excess polymer solution, the concentration of PCL in the solution, a heat treatment temperature, and the number of times that the scaffolds were dipped in the solution were chosen as the significant processing variables. The removal technique of excess polymer solution and the number of times that the scaffolds were dipped in the solution showed the major effects on the flexural strength, while the technique for removal of excess polymer solution was found to have the major effect on the mass of the deposited PCL. The optimal conditions for achieving the maximal flexural strength of the coated scaffolds at the minimal amount of the deposited PCL were determined and tested. High-quality porous bioresorbable scaffolds with approximately 19 MPa flexural strength and approximately 0.4g of the total (PCL coating+calcium phosphates) mass with dimensions of 8.5 mm diameter and 13 mm width were manufactured as a result.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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.

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

Citations32
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in MedicineSame topicBone Tissue Engineering MaterialsFrench-language works237,207