Fully Printed Flexible Polystyrene/Graphite-Based Temperature Sensor with Excellent Properties for Potential Smart Applications
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This study presents an innovative temperature sensor based on a thermistor nanocomposite of graphite (Gt) and polystyrene (PS). The sensor exhibited notable thermal stability and film integrity, offering two distinct linear response regions within the tested temperature range of −10 to 60 °C. It demonstrated a sensitivity of 0.125% °C –1 between −10 and 10 °C, followed by another linear response with a sensitivity of 0.41% °C –1 from 20 to 60 °C. Furthermore, it exhibited a response/recovery time of 0.97/1.3 min at a heating/cooling rate of 60 °C min –1 . The sensor maintained minimal baseline drift even when subjected to varying humidity levels. We assessed its mechanical flexibility and stability for hundreds of bending cycles at a bending angle of 30°, adapting to dynamic environmental conditions. The sensor’s thermomechanical test (response to mechanical stress under temperature fluctuations) underscored its adaptability over a temperature range of −10 to 60 °C. Notably, it displayed excellent chemical stability, maintaining consistent performance when subjected to harsh environmental conditions like exposure to corrosive gases and prolonged immersion in tap water. Real-world tests demonstrated its practical utility, including precise temperature measurements in solid objects and breath temperature monitoring. These findings suggest promising applications in healthcare, environmental monitoring, and various IoT applications.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".