High-resolution 3D printing of xanthan gum/nanocellulose bio-inks
Bibliographic record
Abstract
The current study provides a comprehensive rheology study and a survey on direct ink writing of xanthan gum/cellulose nanocrystal (XG/CNC) bio-inks for developing 3D geometries that mimic soft tissue engineering scaffolds' physical and mechanical properties. The presence of CNC was found to be a critical prerequisite for the printability of XG bio-inks; accordingly, the hybrid XG/CNC bio-inks revealed the excellent viscoelastic properties that enabled precise control of hydrogel shaping and printing of lattice structures composed of up to eleven layers with high fidelity and fair resolution without any deformation after printing. The lyophilized 3D scaffolds presented a porous structure with open and interconnected pores and a porosity higher than 70%, vital features for tissue engineering scaffolds. Moreover, they showed a relatively high swelling of approximately 11 g/g, facilitating oxygen and nutrient exchange. Furthermore, the elastic and compressive moduli of the scaffolds that enhanced significantly upon increasing CNC content were in the range of a few kPa, similar to soft tissues. Finally, no significant cell cytotoxicity was observed against human liver cancer cells (HepG2), highlighting the potential of these developed 3D printed scaffolds for soft tissue engineering applications.
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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".