The Effects of Shear Stress on the Micromechanical Properties of 3D Printable Biopolymer Nanocomposites Using a Custom‐Designed Extrusion‐Based 3D Printer
Bibliographic record
Abstract
Current advancements in 3D printing technology have the potential to facilitate the production of scaffolds and implants for various biomedical applications, including bone repair and regeneration. 3D printed patient‐specific bone‐inspired nanocomposite grafts might be a viable alternative to current bone repair treatment methods if they provide appropriate anatomic structure, biocompatibility, and adequate mechanical properties. In the current work, a 3D printable nanocomposite biomaterial ink with bone cell biocompatibility (in vitro) is printed while adjusting shear stress during extrusion using a custom‐designed 3D printer to investigate the shear stress effect on the mechanical properties of the 3D printed nanocomposite. Tensile test results, as well as polarized light microscopy and differential scanning calorimetry analyses, reveal that increasing the applied shear stress from 3.5 to 14 kPa during extrusion‐based 3D printing in a custom‐built 3D printer, increased the strength, tensile modulus, and toughness of printed nanocomposite filaments by about three‐fold. This improvement is attributed to increased crystallinity in the thermoset biopolymer matrix due to the higher shear stress and the nano‐confinement effect. This implies that greater shear during layer‐by‐layer extrusion‐based 3D printing might be employed to create more robust mechanically competent 3D printed nanocomposite bone grafts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".