Modulating mechanical behaviour of 3D-printed cartilage-mimetic PCL scaffolds: influence of molecular weight and pore geometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Three-dimensional (3D)-printed poly(ε)-caprolactone (PCL)-based scaffolds are increasingly being explored for cartilage tissue engineering (CTE) applications. However, ensuring that the mechanical properties of these PCL-based constructs are comparable to that of articular cartilage that they are meant to regenerate is an area that has been under-explored. This paper presents the effects of PCL's molecular weight (MW) and scaffold's pore geometric configurations; strand size (SZ), strand spacing (SS), and strand orientation (SO), on mechanical properties of 3D-printed PCL scaffolds. The results illustrate that MW has significant effect on compressive moduli and yield strength of 3D-printed PCL scaffolds. Specifically, PCL with MW of 45 K was a more feasible choice for fabrication of visco-elastic, flexible and load-bearing PCL scaffolds. Furthermore, pore geometric configurations; SZ, SS, and SO, all significantly affect on tensile moduli of scaffolds. However, only SZ and SS have statistically significant effects on compressive moduli and porosity of these scaffolds. That said, inverse linear relationship was observed between porosity and mechanical properties of 3D-printed PCL scaffolds in Pearson's correlation test. Altogether, this study illustrates that modulating MW of PCL and pore geometrical configurations of the scaffolds enabled design and fabrication of PCL scaffolds with mechanical and biomimetic properties that better mimic mechanical behaviour of human articular cartilage. Thus, the modulated PCL scaffold proposed in this study is a framework that offers great potentials for CTE applications.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it