3D printing and properties of cellulose nanofibrils-reinforced quince seed mucilage bio-inks
Bibliographic record
Abstract
Plant-based hydrogels have attracted great attention in biomedical fields since they are biocompatible and based on natural, sustainable, cost-effective, and widely accessible sources. Here, we introduced new viscoelastic bio-inks composed of quince seed mucilage and cellulose nanofibrils (QSM/CNF) easily extruded into 3D lattice structures through direct ink writing in ambient conditions. The QSM/CNF inks enabled precise control on printing fidelity where CNF endowed objects with shape stability after freeze-drying and with suitable porosity, water uptake capacity, and mechanical strength. The compressive and elastic moduli of samples produced at the highest CNF content were both increased by ~100% (from 5.1 ± 0.2 kPa and 32 ± 1 kPa to 10.7 ± 0.5 and 64 ± 2 kPa, respectively). These values ideally matched those reported for soft tissues; accordingly, the cell compatibility of the printed samples was evaluated against HepG2 cells (human liver cancer). The results confirmed the 3D hydrogels as being non-cytotoxic and suitable to support attachment, survival, and proliferation of the cells. All in all, the newly developed inks allowed sustainable 3D bio-hydrogels fitting the requirements as scaffolds for soft tissue engineering.
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 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".