Recent Advances in Biopolymer Modifications for the Generation of 3D Printable Hydrogels
Bibliographic record
Abstract
The growing demand for sustainable materials has driven interest in biopolymer-based 3D printing, yet their limited extrudability and shape fidelity restrict direct application. This review summarizes recent advances in the molecular design of protein- and polysaccharide-based hydrogels through chemical modification, introducing cross-linkable groups that enhance shear-thinning for extrusion, self-healing for shape fidelity, and controlled gelation for structural stability, thereby overcoming these limitations. Emerging granular hydrogels are also highlighted as next-generation 3D-printable bioinks. Significantly, this review bridges the knowledge gap by elucidating the molecular rationale linking chemical modifications to key ink properties and their functional performance, particularly in customizing food structures with tailored textures and nutrient delivery profiles, and in designing tissue-engineering scaffolds that balance structural fidelity with biological functionality to support cell-laden printing and tissue regeneration. Overall, this review first establishes a molecular framework linking biopolymer modification to 3D printing outcomes, guiding next-generation biofabrication technologies across food and biomedical fields.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".