Stretchable polydimethylsiloxane/aligned electrospun cellulose acetate nanofibers composites with high transparency and fracture resistance
Bibliographic record
Abstract
Abstract Polydimethylsiloxane (PDMS) is recognized as an excellent stretchable substrate in wearable electronics, due to its desirable properties such as tensile properties, transparency, thermal stability, non‐toxicity, and good biocompatibility. However, its limited fracture toughness and susceptibility to cracking significantly reduce the material's overall durability. In this study, the solution casting method was applied to prepare the nanofibers composites combined stretchable PDMS and aligned electrospun cellulose acetate (CA) to improve its mechanical properties and keep its transparency. The results showed that composites containing 3 wt% loadings of electrospun CA nanofibers exhibited a light transmittance exceeding 85% within the visible light range. Specifically, the PDMS/CA‐400 composites exhibited maximum improvements in comparison to pure PDMS. Notably, the tensile strength increased significantly from 2.1 to 3.0 MPa, while the toughness increased from 0.93 to 2.49 MJ/m3. In addition, the tensile strength of PDMS/CA‐400 composites with pre‐cut cracks increased from 0.2 to 1.4 MPa, and the fracture toughness increased from 14.78 to 174.42 kJ/m3, which were respectively 7 and 12 times compared to pure PDMS. Scanning electron microscope images showed that PDMS formed good interfacial interaction with CA nanofibers. This study introduces a novel method utilizing electrospun nanofibers to create transparent and fracture‐resistant stretchable composites, offering promising enhancements for the durability of wearable electronic devices. Highlights Explore the impact of the speed of receiving rollers on the degree of fiber arrangement. PDMS/CA‐400 composite material shows good light transmittance and high fracture resistance. The tight interface between the fiber and the matrix improves the optical transmission and mechanical properties of the composite materials.
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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 | 0.000 |
| 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".