Multilayered Single-Walled Carbon Nanotube-Based Flexible Temperature Sensor
Bibliographic record
Abstract
This article presents the development of a multilayered single-walled carbon nanotube (SWNT)-based temperature sensor fabricated using a spray coating process on a flexible polydimethylsiloxane (PDMS) substrate. The carbon nanotube (CNT)-based sensor showed negative temperature coefficient (NTC) behavior, with a decrease in resistance change as the temperature increased from 293 to 383 K. It exhibited a high temperature coefficient of resistance (TCR) at −1.99/K (295–323 K) and a response time of 2.8 s. The sensor’s flexibility was tested by varying the bending radius from 21.8 to 10.9 mm and the sensor displayed consistent performance under mechanical deformation. The uniformity, roughness, and surface morphology of the coated CNTs were measured. The sensors’ accuracy and repeatability were assessed through controlled heating and cooling cycles. The stability and reliability of the sensor are improved by optimizing the coating parameters, fabrication method, and device design. The sensor showed consistency and long-term stability demonstrating its reliability for practical applications in conformal settings. The flexible CNT sensors presented in this article offer the potential to be applied for temperature sensing in conformal and flexible fitting applications, for example, temperature detection in electric vehicle (EV) battery cells.
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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) | 0.000 | 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".