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3D Printed Porous Scaffold Polymers Based on Polymerization-Induced Phase Separation and Their Applications in Biomedical Fields

2025· article· en· W4414239907 on OpenAlex
Molin Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTheoretical and Natural Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
Keywords3D printing3d printedScaffoldPolymerPorosityPhase (matter)Characterization (materials science)

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.294
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it