Gum Sensor: A Stretchable, Wearable, and Foldable Sensor Based on Carbon Nanotube/Chewing Gum Membrane
Bibliographic record
Abstract
Presented in this work is a novel and facile approach to fabricate an elastic, attachable, and cost-efficient carbon nanotube (CNT)-based strain gauge which can be efficiently used as bodily motion sensors. An innovative and unique method is introduced to align CNTs without external excitations or any complicated procedure. In this design, CNTs are aligned and distributed uniformly on the entire chewing gum by multiple stretching and folding technique. The current sensor is demonstrated to be a linear strain sensor for at least strains up to 200% and can detect strains as high as 530% with a high sensitivity ranging from 12 to 25 and high durability. The gum sensor has been used as bodily motion sensors, and outstanding results are achieved; the sensitivity is quite high, capable of tracing slow breathing. Since the gum sensor can be patterned into various forms, it has wide applications in miniaturized sensors and biochips. Interestingly, we revealed that our gum sensor has the ability to monitor humidity changes with high sensitivity and fast resistance response capable of monitoring human breathing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".