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Record W4389584898 · doi:10.17118/11143/20722

The effect of pore size on the stiffness of composite scaffold

2023· article· en· W4389584898 on OpenAlexafffund
Jonghyun Kim, Yunhua Luo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConstruction Engineering and Safety
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComposite numberStiffnessMaterials scienceScaffoldComposite materialEngineeringBiomedical engineering

Abstract

fetched live from OpenAlex

Abstract: Low mechanical stiffness is often an issue in the design of implant scaffolds that are required to have moderate porosity. The purpose of our study is to determine the optimal pore size for a given porosity, so that the scaffold has maximum stiffness. The composite scaffold we studied consists of hydroxyapatite (HA) and polylactic acid (PLA). The microstructure-free finite element modeling (MF-FEM), a finite element approach recently developed for the modeling of composite materials, was adopted for this study. We first compared MF-FEM predictions with conventional formulas of stiffness-porosity relation for the purpose of validation. Then, MF-FEM was used to investigate the effect of pore size on scaffold stiffness in single-phase scaffold, because none of the conventional formulas can predict the effective stiffness of two-phase porous composites. The results show that MF-FEM has excellent agreement with well-established analytical formulas, pore size roughly has a linear effect on both Young’s modulus and shear modulus of composite scaffold, i.e. larger pore size leads to higher stiffness.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.133

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 designSimulation or modeling
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
Published2023
Admission routes2
Has abstractyes

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