Flexible and Stretchable Temperature Sensors Fabricated Using Solution‐Processable Conductive Polymer Composites
Bibliographic record
Abstract
Accurate monitoring of physiological temperatures is important for the diagnosis and tracking of various medical conditions. This work presents the design, fabrication, and characterization of temperature sensors using conductive polymer composites (CPCs) patterned on both flexible and stretchable substrates through both drop coating and direct ink writing (DIW). These composites were formed using a high melting point biopolymer polyhydroxybutyrate (PHB) as the matrix and the graphenic nanomaterial reduced graphene oxide (rGO) as the nanofiller (from 3 to 12 wt%), resulting in a material that exhibits a temperature-dependent resistivity. At room temperature the composites exhibited electrical percolation behavior. Around the percolation threshold, both the carrier concentration and mobility were found to increase sharply. Sensors were fabricated by drop-coating PHB-rGO composites onto ink-jet printed silver electrodes. The temperature coefficient of resistance was determined to be 0.018 /°C for pressed rGO powders and 0.008 /°C for the 3 wt% samples (the highest responsivity of all composites). Composites were found to have good selectivity to temperature with respect to pressure and moisture. Thermal mapping was demonstrated using 6 × 7 arrays of sensing elements. Stretchable devices with a meandering pattern were fabricated using DIW, demonstrating the potential for these materials in healthcare monitoring devices.
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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 | 0.000 | 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".