Melt electrowriting of poly(ϵ-caprolactone)—poly(ethylene glycol) backbone polymer blend scaffolds with improved hydrophilicity and functionality
Bibliographic record
Abstract
Melt electrowriting (MEW) is an additive manufacturing technique that harnesses electro-hydrodynamic phenomena to produce 3D-printed fibres with diameters on the scale of 10s of microns. The ability to print at this small scale provides opportunities to create structures with incredibly fine resolution and highly defined morphology. The current gold standard material for MEW is poly(ϵ-caprolactone) (PCL), a polymer with excellent biocompatibility but lacking in chemical groups that can allow intrinsic additional functionality. To provide this functionality while maintaining PCL's positive attributes, blending was performed with a Poly(Ethylene Glycol) (PEG)-based Acrylate endcapped Urethane-based Precursor (AUP). AUPs are a group of polymers, built on a backbone of existing polymers, which introduce additional functionality by the addition of one or more acrylate groups that terminate the polymer chain of a backbone polymer. By blending with a 20kDa AUP-PEG in small amounts, it is shown that MEW attributes are preserved, producing high-quality meshes. Blends were produced in various PCL:AUP weight ratios (100:0, 90:10 and 0:100) and processed into both solvent-cast films and MEW meshes that were used to characterise the properties of the blends. It was found that the addition of AUP-PEG to PCL significantly increases the hydrophilicity of structures produced with these polymers, and adds swelling capability compared to the non-swelling PCL. The developed blend (90:10) is shown to be processable using MEW, and the quality of manufactured scaffolds is evaluated against pure PCL scaffolds by performing scanning electron microscopy image analysis, with the quality of the novel MEW blend scaffolds showing comparable quality to that of pure PCL. The presence of the functionalisable AUP material on the surface of the developed scaffolds is also confirmed using fluorescence labelling of the acrylate groups. Biocompatibility of the MEW-processable blend was confirmed through a cell viability study, which found a high degree of cytocompatibility.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".