Processing and Properties of Chitosan Inks for 3D Printing of Hydrogel Microstructures
Bibliographic record
Abstract
The ability to precisely control the properties of natural polymers and fabricate three-dimensional (3D) structures is critical for biomedical applications. In this work, we report the printing of complex 3D structures made of soft polysaccharide (chitosan) inks directly in air and at room temperature. We perform a comprehensive characterization of the 3D printing process by analyzing the effect of ink properties (i.e., rheological properties and solvent evaporation) and process-related printing parameters (i.e., nozzle diameter, robot velocity, and applied pressure). The effects of the neutralization step on the hydrogel formation and their mechanical properties are also investigated. Solvent evaporation tests show that the chitosan ink prepared using an acidic mixture contains residual acids after printing, helping reducing shrink-induced shape deformation. A processing map presents the appropriate ranges of process-related parameters for different structures including filaments, 30-layer scaffolds, starfish, leaf, and spider shapes, showing the versatility of the fabrication approaches. After neutralization, 3D scaffolds still maintain their shape while neutralized filaments show high tensile properties such as a maximum tensile strength of ∼97 MPa in the dry state and high strain at break ∼360% in the wet state. Our fabrication approach provides guidelines to optimize the design and fabrication of aqueous-based inks and opens a new door for fabricating complex structures from natural polymers and achieving tunable material properties for biomedical applications such as tissue engineering and drug delivery.
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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 | 0.001 |
| 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".