On the Rheological Properties and Printability of Sodium Alginate–Carboxymethyl Chitosan Composite Solutions for Tissue Scaffold Printing
Bibliographic record
Abstract
Composites of sodium alginate (Alg) and carboxymethyl chitosan (CMCS) are used to 3D print tissue scaffolds, but the rheological properties and printability of these composites remain underreported, resulting in time-consuming trial-and-error printing. This study investigates these properties to rigorously design the 3D printing process. Dynamic shear tests characterize viscoelastic and frequency-dependent properties, while steady shear tests assess the apparent viscosity and temperature-dependent viscosity. A novel approach based on mass flow rate models guides the printing of two-layer scaffolds for printability analysis. Brightfield microscopy and printability indexes quantify the deviations between printed and designed scaffolds, defined as printability. Results show that Alg predominantly directs the rheological properties. At 4% w/v Alg, the addition of < 3% w/v CMCS reduces elasticity, contrary to the trend where increasing CMCS increases elasticity. CMCS improves the thermal resistance of the composites, while Alg reduces it. Of the composites printed, a 4% w/v Alg + 1% w/v CMCS formulation most accurately replicates the designed scaffold, and adding CMCS improves scaffold printing repeatability by at least threefold compared to Alg-only solutions. These findings provide a framework that informs the preparation and performance of Alg-CMCS composites with tunable properties, advancing scaffold bioprinting for tissue engineering.
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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 | 0.001 |
| 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".