A Simple Method to Produce a Piezoelectric Composite Membrane with Aligned and Crimped Nanofibers for Wearable Pressure Sensors
Bibliographic record
Abstract
Organic fiber-based pressure sensors are extensively used in smart wearable electronic devices due to their remarkable combination of wearability and sensitivity. However, enhancing their sensing and wearable performance has been hindered by complex processing parameters, use of toxic chemicals, and high costs. Herein, a simple, efficient, and green method is developed using wool lamination and ethanol treatment to prepare cross-scale sandwich-structured composite membranes that contain aligned yet crimped nanofibers. The proposed method significantly improves the permeability and mechanical properties of a poly(vinylidene fluoride) (PVDF) fiber-based membrane, increasing the pore size from 3.18 ± 0.69 to 3.91 ± 0.90 μm, tensile strength from 3.85 ± 0.22 to 8.28 ± 0.98 MPa, and ultimate strain from 60.2 ± 7.7 to 200 ± 30.3%, when compared to the PVDF nanofiber membrane produced without wool lamination or ethanol treatment. Furthermore, the piezoelectric coefficient and sensitivity increase by approximately 70 and 300% respectively, to 45.6 pC/N and 2.0 V/N. The feasibility of applying this method to other materials is demonstrated by testing polyacrylonitrile nanofibers. This work presents an effective method to produce functional structures in nanofiber composite membranes, endowing them with enhanced piezoelectricity and hence elevated potential in smart wearables.
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 | 0.001 |
| 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".