Influence of electrospinning parameters on the development of high-quality electrospun nanofibers: A brief critical assessment
Bibliographic record
Abstract
Electrospinning is a versatile technique commonly used to produce nanofibers for various biomedical applications and in tissue engineering. This technique operates on the principle of electrostatic forces, which create fibrous scaffolds from biocompatible polymers. Key characteristics of electrospun nanofibers include their lightweight nature, softness, porosity, and large surface area-to-volume ratio. However, there is a notable gap in the understanding of how these different process parameters affect the quality and mechanical properties of the nanofibers for applications in tissue engineering, drug delivery systems, wound dressings, and scaffolds for regenerative medicine. To bridge this gap, it is crucial to explore different combinations of electrospinning parameters, such as solution flow rate, collector rotation speed, and polymer concentration, as they significantly affect the quality and characteristics of the resulting nanofibers. These various combinations of electrospinning parameters can significantly influence pore size, fiber thickness, surface porosity, and the overall properties of nanofiber structures. In this paper, we present a critical review of the various electrospinning parameters used in producing electrospun nanofibers, and we document a few optimal conditions that can greatly enhance production quality. This review paper aims to emphasize the importance of selecting appropriate polymers along with electrospinning parameters during the fabrication process while contributing valuable insights for researchers and industry stakeholders. Additionally, the impact of incorporating additive materials into the electrospinning process is discussed, along with potential future directions for developing high-quality nanofiber scaffolds with enhanced mechanical properties.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".