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Record W4309708577 · doi:10.3390/polym14225017

Height-to-Diameter Ratio and Porosity Strongly Influence Bulk Compressive Mechanical Properties of 3D-Printed Polymer Scaffolds

2022· article· en· W4309708577 on OpenAlexaff
José I. Contreras Raggio, Carlos Toro Arancibia, Carola Millán, Heidi‐Lynn Ploeg, Ameet K. Aiyangar, Juan F. Vivanco

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsQueen's University
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsMaterials sciencePorosityCompressive strengthComposite material3d printedPolymerBiomedical engineering

Abstract

fetched live from OpenAlex

Although the architectural design parameters of 3D-printed polymer-based scaffolds—porosity, height-to-diameter (H/D) ratio and pore size—are significant determinants of their mechanical integrity, their impact has not been explicitly discussed when reporting bulk mechanical properties. Controlled architectures were designed by systematically varying porosity (30–75%, H/D ratio (0.5–2.0) and pore size (0.25–1.0 mm) and fabricated using fused filament fabrication technique. The influence of the three parameters on compressive mechanical properties—apparent elastic modulus Eapp, bulk yield stress σy and yield strain εy—were investigated through a multiple linear regression analysis. H/D ratio and porosity exhibited strong influence on the mechanical behavior, resulting in variations in mean Eapp of 60% and 95%, respectively. σy was comparatively less sensitive to H/D ratio over the range investigated in this study, with 15% variation in mean values. In contrast, porosity resulted in almost 100% variation in mean σy values. Pore size was not a significant factor for mechanical behavior, although it is a critical factor in the biological behavior of the scaffolds. Quantifying the influence of porosity, H/D ratio and pore size on bench-top tested bulk mechanical properties can help optimize the development of bone scaffolds from a biomechanical perspective.

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 score0.031
Threshold uncertainty score0.661

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 head0.013
GPT teacher head0.203
Teacher spread0.190 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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