Novel Fabrication Technique for 3-Dimensional Electrospun Poly(DL-Lactide-Co-Glycolide) Acid Scaffolds
Bibliographic record
Abstract
Current tissue engineering scaffolds created by electrospinning techniques are mostly limited to a 2D membrane. In this study, a novel method to fabricate 3D fibrous scaffolds is presented, and a parametric study to identify the optimal processing parameters was performed. The fabrication technique uses a batch foaming setup to saturate the fibrous scaffolds with CO2 to lower the glass transition temperature of PLGA to allow for the sintering of the fibers. When a mechanical pressure was applied to multiple layers of the thin films in the presence of gas, the layers of PLGA scaffolds sinter to form a thick 3D fibrous structure. This study was divided into three parts. First, the effect of gas saturation on the scaffolds was examined. Three saturation pressures of 200, 300, and 400 psi and multiple saturation times of 1, 3, 5, 30, 60, and 120 minutes were used. At the lowest pressure of 200 psi, the morphology and mechanical properties of the scaffolds were not affected. As saturation pressure and time were increased, the fibers sintered, and eventually the fibrous structure was lost because the polymer was over-sintered. The second part of the study was to determine the adhesion properties of the scaffolds using gas pressure. The same processing pressures and times were used for this set of experiments, and a higher pressure was found to better adhere the layers of PLGA films together. From the first two parts of this study, the optimal combination of processing parameters was determined to be saturating the samples under 400 psi of pressure for three minutes. This set of parameters was then used in the third part of the study to fabricate 3D fibrous scaffolds. The demonstration of this ability to fabricate 3D scaffolds improves on current electrospinning techniques while maintaining a desirable fibrous structure for tissue engineering.
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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 | 0.001 |
| 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".