Novel fabrication technique for three‐dimensional micropatterned electrospun poly(<scp>DL</scp>‐lactide‐<i>co</i>‐glycolide) acid
Bibliographic record
Abstract
Abstract Current tissue engineering scaffolds created by electrospinning techniques are mostly limited to two‐dimensional membranes. In this article, a novel method for fabricating three‐dimensional (3D) fibrous scaffolds is presented. The fabrication technique uses the saturation of carbon dioxide in poly(lactide‐co‐glycolide) acid to lower the glass‐transition temperature of the polymer to allow for the sintering of multiple layers of electrospun films. The effect of gas saturation on the films was first examined by variation of the saturation pressure and time. Fiber sintering was observed as the saturation pressure and time were increased. Next, the adhesion strength between two layers of scaffolds that were sintered by gas saturation was examined, and a higher pressure of 400 psi was found to better adhere the layers of the membranes together. From the first two parts of this study, the optimal combination of gas saturation parameters was determined to a pressure of 400 psi for 3 min. With this set of parameters, 3D structures were fabricated by the sintering of 30 layers of electrospun scaffolds. Also, by patterning individual layers using microfeatured plates, we improved the open porosity. This demonstration of the ability to fabricate 3D scaffolds improves current electrospinning techniques and maintains a desirable fibrous structure for tissue engineering. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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 | 0.001 |
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".