Molecular Orientation in Electrospun Fibers: From Mats to Single Fibers
Bibliographic record
Abstract
Electrospinning is the most versatile and popular technique for preparing nanofibers from a very broad range of polymer systems. In spite of more than 20 years of studies, our understanding of the relationships between the microstructure and the properties of these fibers, and how they are influenced by the electrospinning conditions, remains fragmentary. This is especially true for molecular orientation—a critical parameter that is often invoked to explain the properties of fibers but that is challenging to quantify properly. Recently, the emergence of characterization techniques enabling studies at the single fiber level, including their orientation, has propelled the field in new directions and provided a wealth of new knowledge. In this Perspective, we review and discuss our current understanding of the structure and properties of electrospun nanofibers with a particular emphasis on their molecular orientation. We first describe how studies at the mat level have provided crucial knowledge about the impact of orientation but also revealed the difficulties associated with its measurement. In the following sections, we present and critically review the most important findings originating from studies of the mechanical and thermal properties of individual fibers. We focus in particular on important models proposed in the literature to explain the variation of the modulus with fiber diameter. We then describe the latest advances in the microstructural characterization of individual fibers. Finally, we show the importance of controlling molecular orientation for some of the most exciting new applications of electrospun nanofibers.
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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 | 0.001 |
| 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".