Importance of axial stretch on the determination of Young’s modulus of electrospun poly(<i>ε</i>-caprolactone) nanofibres by atomic force microscopy
Bibliographic record
Abstract
With increasing interest in the use of polymeric nanofibres for biomedical applications such as composite materials and tissue scaffolding, accurate determination of their mechanical properties is essential. Fibre orientation and the stiffness of individual fibres determine the overall elastic modulus of nanofibrous materials. However, accurate measurements of the elastic properties of single fibres are challenging at the nanoscale, and distinguishing between results arising from competing models can be difficult. We report here on investigations of the Young’s modulus of single poly(ε-caprolactone) (PCL) electrospun nanofibres by measuring the deflection of fibres due to a loading force applied by an atomic force microscope (AFM). Although such testing is often performed with the tacit assumption that bending resistance alone is responsible for the fibre response, we found that consistent results could only be obtained if the overall fibre stretch is taken into account. The Young’s modulus we measured for electrospun PCL fibres with diameters ranging from 100 to 400 nm was 0.48 ± 0.03 GPa, which is similar to the modulus of bulk PCL, with no apparent dependence on diameter. Our findings highlight the importance of the assumptions used in the analysis of bending data, as discounting the effects of axial stretch and pre-existing tension typically lead to an overestimate of the Young’s modulus.
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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 | 0.002 |
| 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 | 0.000 |
| 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".