3D Printed Porous Scaffold Polymers Based on Polymerization-Induced Phase Separation and Their Applications in Biomedical Fields
Bibliographic record
Abstract
The integration of polymerization-induced phase separation (PIPS) with three-dimensional (3D) printing provides a transformative strategy for fabricating porous scaffolds in biomedical applications. Unlike traditional 3D printing, which faces challenges in pore control and material diversity, PIPS enables in situ formation of interconnected networks without sacrificial templates, allowing tunable pore size, distribution, and connectivity across multiple scales. This review introduces the principles of PIPS and its coupling with advanced platforms such as digital light processing (DLP), emphasizing parameters that govern phase separation behavior. It further highlights functional polymer systems, including photosensitive, degradable, conductive, and antimicrobial composites, and analyzes how their compositions influence pore morphology and biological performance. By combining precise structural control with multifunctional materials, PIPS-based 3D printing supports the development of next-generation biomedical scaffolds with enhanced adaptability, biocompatibility, and potential for smart, sustainable designs.
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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) | 0.001 | 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".