Ultra-sensitive flexible stretchable sensor based on bionic structure using graphene oxide and carboxylated multi-walled carbon nanotubes for wearable electronic skin
Bibliographic record
Abstract
• Flexible stretchable sensor based on biomimetic structures. • Graphene oxide and carboxylated multi-walled carbon nanotubes are employed as synergistic conductive sensing materials. • The flexible stretchable sensor with high sensitivity and sensing range. • Flexible stretchable sensors are applied to demonstrate wearable electronic skin. Flexible stretchable sensors have recently attracted significant attention due to their great potential in detecting human joint posture and monitoring health. However, fabricating stretchable sensors that combine ultrasensitive responsiveness with fast response times over a wide strain range remains a major challenge. To address this issue, this study presents an ultrasensitive flexible stretchable sensor (FSS) based on a biomimetic structure, utilizing graphene oxide and carboxylated multi-walled carbon nanotubes as synergistic conductive sensing materials. The FSS exhibited excellent performance, including a strain gauge factor of up to 84.942, a sensing range of up to 160 %, a lower strain detection limit of 0.25 %, and rapid response and recovery times50 ms and 70 ms, respectively. Additionally, FSS is successfully applied to Morse code messaging, motion monitoring, and sitting posture recognition, highlighting its potential for wearable electronic skin applications.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".