Innovations in hydrogel-based manufacturing: A comprehensive review of direct ink writing technique for biomedical applications
Bibliographic record
Abstract
Direct ink writing (DIW) stands as a pioneering additive manufacturing technique that holds transformative potential in the field of hydrogel fabrication. This innovative approach allows for the precise deposition of hydrogel inks layer by layer, creating complex three-dimensional structures with tailored shapes, sizes, and functionalities. By harnessing the versatility of hydrogels, DIW opens up possibilities for applications spanning from tissue engineering to soft robotics and wearable devices. This comprehensive review investigates DIW as applied to hydrogels and its multifaceted applications. The paper introduces a diverse range of printing techniques while providing a thorough exploration of DIW for hydrogel-based printing. The investigation aims to explain the progress made, challenges faced, and potential trajectories that lie ahead for DIW in hydrogel-based manufacturing. The fundamental principles underlying DIW are carefully examined, specifically focusing on rheological attributes and printing parameters, prompting a comprehensive survey of the wide variety of hydrogel materials. These encompass both natural and synthetic variations, all of which can be effectively harnessed for this purpose. Furthermore, the review explores the latest applications of DIW for hydrogels in biomedical areas, with a primary focus on tissue engineering, wound dressing, and drug delivery systems. The document not only consolidates the existing state of DIW within the context of hydrogel-based manufacturing but also charts potential avenues for further research and innovative breakthroughs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".