Multifunctional Fibrous Mats: Stretchable, Conductive, and Hydrophilic Platforms for Wearable Electronics
Bibliographic record
Abstract
Abstract The field of soft wearable bioelectronics requires materials that are flexible, stretchable, biocompatible, and capable of being used over long durations. Although polydimethylsiloxane (PDMS) is one of the most commonly used substrates for these devices due to its biomimetic properties compared to biological tissues, its intrinsic hydrophobicity causes it to underperform in biological environments. In this work, a hydrophilic, stretchable PDMS electrospun fibrous mat is developed to overcome this limitation by incorporating the amphiphilic polymer polyethylene glycol block copolymer (PEG‐BCP) into the porous PDMS matrix. The nonwoven hydrophilic silicone mat shows apparent improvement in stable hydrophilicity, indicated by a significant decrease in water contact angle (from 125° to 51°) for 7 days, along with improved cellular adhesion and enhanced breathability. The PDMS‐PEG fibers show higher cell proliferation than unmodified PDMS fibers, suggesting potential for long‐term biological applications. The fibrous mat also maintains its structural integrity under mechanical stress, demonstrated by a stretchability of up to 308.8% strain with reduced adhesion forces. This novel material surpasses previous PDMS fibrous substrates and enables electroless gold plating, providing a promising future for wearable fibrous electronics and biomedical devices featuring hydrophilic, stretchable, conductive, and biointegrated materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".