Printability of 3D Printed Hydrogel Scaffolds: Influence of Hydrogel Composition and Printing Parameters
Bibliographic record
Abstract
Extrusion-based bioprinting of hydrogel scaffolds is challenging due to printing-related issues, such as the lack of capability to precisely print or deposit hydrogels onto three-dimensional (3D) scaffolds as designed. Printability is an index to measure the difference between the designed and fabricated scaffold in the printing process, which, however, is still under-explored. While studies have been reported on printing hydrogel scaffolds from one or more hydrogels, there is limited knowledge on the printability of hydrogels and their printing processes. This paper presented our study on the printability of 3D printed hydrogel scaffolds, with a focus on identifying the influence of hydrogel composition and printing parameters/conditions on printability. Using the hydrogels synthesized from pure alginate or alginate with gelatin and methyl-cellulose, we examined their flow behavior and mechanical properties, as well as their influence on printability. To characterize the printability, we examined the pore size, strand diameter, and other dimensions of the printed scaffolds. We then evaluated the printability in terms of pore/strand/angular/printability and irregularity. Our results revealed that the printability could be affected by a number of factors and among them, the most important were those related to the hydrogel composition and printing parameters. This study also presented a framework to evaluate alginate hydrogel printability in a systematic manner, which can be adopted and used in the studies of other hydrogels for bioprinting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".