Development of 3D-Printable Albumin–Alginate Foam for Wound Dressing Applications
Bibliographic record
Abstract
In this article, a method to develop 3D printable hybrid sodium alginate and albumin foam, crosslinked with calcium chloride mist is introduced. Using this method, highly porous structures are produced without the need of further postprocessing (such as freeze drying). The proposed method is particularly beneficial in the development of wound dressing as the printed foams show excellent lift-off and water absorption properties. Compared with methods that use liquid crosslinker, the use of mist prevents the leaching of biocompounds into the liquid crosslinker. 3D printing technique was chosen to provide more versatility over the wound dressing geometry. Calcium chloride and rhodamine B were used as the crosslinking material and the model drug, respectively. Various biomaterial inks were prepared by different concentrations of sodium alginate and albumin, and the fabricated scaffolds were crosslinked in mist, liquid, or kept without crosslinking. The effects of biomaterial composition and the crosslinking density on the wound dressing properties were assessed through printability studies. The mist-crosslinked biomaterial ink composed of 1% (w/v) sodium alginate and 12% (w/v) albumin showed the superior printability. The fabricated scaffolds were also characterized through porosity, mechanical, degradation, and drug release tests. The mist-crosslinked scaffolds showed superior mechanical properties and provided relatively prolonged drug release.
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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) | 0.000 | 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".