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Record W4384297357 · doi:10.1371/journal.pone.0288531

Investigation into relationships between design parameters and mechanical properties of 3D printed PCL/nHAp bone scaffolds

2023· article· en· W4384297357 on OpenAlexafffund
Zahra Yazdanpanah, N. K. Sharma, Amanda Zimmerling, David M. L. Cooper, James D. Johnston, Daniel Chen

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research FoundationUniversity of Saskatchewan
KeywordsMaterials sciencePolycaprolactoneComposite materialScaffoldElastic modulusPorosityScanning electron microscopeComposite numberMicrostructureModulusCompressive strengthBiomedical engineeringPolymer

Abstract

fetched live from OpenAlex

BACKGROUND: Scaffolds are of great importance in tissue engineering applications as they provide a mechanically supportive environment for cellular activity, which is particularly necessary for hard tissues such as bone. Notably, the mechanical properties of a scaffold vary with differing design parameters such as those related to scaffold height and internal structure. Thus, the present study aimed to explore the relationship between design parameters and mechanical properties of composite polycaprolactone (PCL) and nano-hydroxyapatite (nHAp) scaffolds fabricated by three-dimensional (3D) printing. METHODS: We designed and printed scaffolds with different internal structures (lattice and staggered) and varying heights (4, 6, 8 and 10 layers), and consistent porosity (50%) for the purpose of comparison. Then, we examined the scaffold microstructure (pore size and penetration between layers) using scanning electron microscopy (SEM) and mechanical properties (elastic modulus and yield strength) using compressive testing. RESULTS: Our results illustrated that the microstructural parameters were related to scaffold design. At higher heights, pore size increased while penetration between layers decreased; thus, mechanical properties were affected. Results of mechanical testing demonstrated that for lattice scaffolds, elastic modulus was similar for 6 vs 4, and 8 vs 4 layers but ~33% lower for 10 layers vs 4 layers. Similarly, yield strength was comparable for 6 vs 4, and 8 vs 4 layers but ~27% lower for 10 layers vs 4 layers. With staggered scaffolds, when compared to 4-layer results, elastic modulus was similar for 6 layers but was ~43% lower for 8 layers and ~38% lower for 10 layers. Staggered scaffolds had ~38%, ~51%, and ~76% lower yield strength when the number of layers were increased from 4 to 6, 8, and 10 layers, respectively. When comparing lattice and staggered scaffolds with the same layer number, elastic modulus was similar, apart from 8-layer scaffolds where the staggered design was ~42% lower than lattice. Yield strength was similar between 4-layer staggered and lattice scaffolds, while staggered scaffolds with 6, 8, and 10 number of layers showed ~43%, ~45%, ~68% lower strength, respectively, than those found in lattice scaffolds with the same layer numbers. CONCLUSIONS: Mechanical properties of 3D printed scaffolds depended on scaffold height for both lattice and staggered internal structures. Staggered scaffolds had lower mechanical properties than the lattice scaffolds with the same height and were more sensitive to the change in scaffold height. Taken together, lattice scaffolds demonstrated the advantages of more stable mechanical properties over staggered scaffolds. Also, scaffolds with lower height were more promising in terms of mechanical properties compared to scaffolds with greater height.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.148
GPT teacher head0.214
Teacher spread0.066 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2023
Admission routes2
Has abstractyes

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