Printability of Methacrylated Gelatin upon Inclusion of a Chloride Salt and Hydroxyapatite Nano‐Particles
Bibliographic record
Abstract
Abstract 3D biomaterial printing requires an ink to have suitable printability characteristics, as well as creating a final construct of controllable swelling and stiffness. To tune such properties, the impact of adding different levels of chloride salts (NaCl and CaCl2) and hydroxyapatite nano‐particles (nHA) to a highly concentrated and photo‐crosslinkable methacrylated gelatin (GelMA) is investigated. By adding up to 100 mmCaCl2or 1.11mNaCl, the GelMA viscosity decreases from that of control GelMA (no salt). Interestingly, a 25G needle and strong photo‐polymerization kinetics are able to overcome the low viscosity of the 50CaG ink during printing. Adding further CaCl2increases GelMA viscosity, while decreasing both the swelling and dynamic modulus of the UV‐cured construct observed in water. As all UV‐cured constructs have a dynamic modulus greater than 1 MPa, this novel system is able to match the dynamic modulus of articular cartilage—a feat not previously reported for a GelMA‐based system. Lastly, nHA inclusion improves ink printability, as well as decreases swelling and increases dynamic modulus of the final construct. Overall, this study leads to the successful development of a new advanced functional ink which will be beneficial in the 3D printing of biomaterials toward tissue engineering 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.
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".