Additive Manufacturing of a Photo-Cross-Linkable Polymer via Direct Melt Electrospinning Writing for Producing High Strength Structures
Bibliographic record
Abstract
Melt electrospinning writing (MEW) is an emerging additive manufacturing technique that enables the design and fabrication of micrometer-thin fibrous scaffolds made of biocompatible and biodegradable polymers. By using a computer-aided deposition process, a unique control over pore size and interconnectivity of the resulting scaffolds is achieved, features highly interesting for tissue engineering applications. However, MEW has been mainly used to process low melting point thermoplastics such as poly(ε-caprolactone). Since this polymer exhibits creep and a reduction in modulus upon hydration, we manufactured scaffolds of poly(L-lactide-co-ε-caprolactone-co-acryloyl carbonate) (poly(LLA-ε-CL-AC)), a photo-cross-linkable and biodegradable polymer, for the first time. We show that the stiffness of the scaffolds increases significantly (up to ∼10-fold) after cross-linking by UV irradiation at room temperature, compared with un-cross-linked microfiber scaffolds. The preservation of stiffness and high average fiber modulus (370 ± 166 MPa) within the cross-linked hydrated scaffolds upon repetitive loading (10% strain at 1 Hz up to 200,000 cycles) suggests that the prepared scaffolds may be of potential interest for soft connective tissue engineering applications. Moreover, the approach can be readily adapted through manipulation of polymer properties and scaffold geometry to prepare structures with mechanical properties suitable for other tissue engineering applications.
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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 | 0.001 | 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 | 0.001 |
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".