Flexible Smart Textile Coated by PVDF/Graphene Oxide With Excellent Energy Harvesting Toward a Novel Class of Self-Powered Sensors: Fabrication, Characterization and Measurements
Bibliographic record
Abstract
Because of some of their diverse benefits, intelligent textiles have attracted a great deal of interest among specialists over the past decade. This paper describes a novel approach to the manufacture of intelligent piezoelectric polymer-based textiles with enhanced piezoelectric responses for applications that extract biomechanical energy. Here we report a highly scalable and ultrafast production of smart textile piezoelectric containing graphene oxide nanosheets (GONS) dispersed in polyvinylidene fluoride (PVDF). In this work, Cotton textiles (CT) were functionalized and by graphene oxide (GO), using PVDF as a binder to obtain a CT-PVDF-GO material. Tetraethyl orthosilicate (TEOS) was further grafted as a coating layer to improve the surface compatibility, resulting in the CT-PVDF-GO-TEOS composite. The research results show that the addition of GONS significantly improves PVDF's overall crystallization rate on CT. More specifically, the piezoelectric β-phase content (100 % higher F[β]) and crystallinity degree on the piezoelectric properties of composite cotton fiber has been improved effectively. Consequently, this fabricated piezo-smart textile has a glorious piezoelectricity even with comparatively low coating content of PVDF-GONS-TEOS. Based on it, the as-fabricated piezoelectric textile device has resulted in the output voltage of up to 13 mV for a given frequency (fm = 8 Hz) at fixed strain amplitude value (0.5 %). It is believed that this research may further reveal the field of energy harvesting for possible applications in the future.. In addition, the set of experimental results that illustrate the smart textile was carried out and discussed, and how it can be used as a wearable device source for this smart textile. Finally, the approach described in this study can also be used to construct other desirable designs, for a wearable low-consumption sensor, etc.
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) | 0.000 | 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".