Quantifying Polymer Chain Orientation in Strong and Tough Nanofibers with Low Crystallinity: Toward Next Generation Nanostructured Superfibers
Bibliographic record
Abstract
Advanced fibers revolutionized structural materials in the second half of the 20th century. However, all high-strength fibers developed to date are brittle. Recently, pioneering simultaneous ultrahigh strength and toughness were discovered in fine (<250 nm) individual electrospun polymer nanofibers (NFs). This highly desirable combination of properties was attributed to high macromolecular chain alignment coupled with low crystallinity. Quantitative analysis of the degree of preferred chain orientation will be crucial for control of NF mechanical properties. However, quantification of supramolecular nanoarchitecture in NFs with low crystallinity in the ultrafine diameter range is highly challenging. Here, we discuss the applicability of traditional as well as emerging methods for quantification of polymer chain orientation in nanoscale one-dimensional samples. Advantages and limitations of different techniques are critically evaluated on experimental examples. It is shown that straightforward application of some of the techniques to sub-wavelength-diameter NFs can lead to severe quantitative and even qualitative artifacts. Sources of such size-related artifacts, stemming from instrumental, materials, and geometric phenomena at the nanoscale, are analyzed on the example of polarized Raman method but are relevant to other spectroscopic techniques. A proposed modified, artifact-free method is demonstrated. Outstanding issues and their proposed solutions are discussed. The results provide guidance for accurate nanofiber characterization to improve fundamental understanding and accelerate development of nanofibers and related nanostructured materials produced by electrospinning or other methods. We expect that the discussion in this review will also be useful to studies of many biological systems that exhibit nanofilamentary architectures and combinations of high strength and toughness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".