3D-Printed Graphene/Polydimethylsiloxane Composites for Stretchable and Strain-Insensitive Temperature Sensors
Bibliographic record
Abstract
Materials possessing exceptional temperature sensitivity and high stretchability are of importance for real-time temperature monitoring on three-dimensional components with complex geometries, when operating under various external deformation modes. Herein, we develop a stretchable temperature sensor consisting of cellular graphene/polydimethylsiloxane composite. The first of its kind, graphene-based polymer composites with desired microstructures are produced through a direct 3D ink-writing technique. The resultant composites possess long-range-ordered and precisely controlled cellular structure. Temperature-sensing properties of three cellular structures, including grid, triangular, and hexagonal porous structures are studied. It is found that all three cellular composites present more stable sensitivities than solid composites under external strains because of the fine porous structure that can effectively share the external strain, and the composites with a grid structure delivered particularly a stable sensing performance, showing only ∼15% sensitivity decrease at a large tensile strain of 20%. Taking full advantage of the composites with a grid structure in terms of sensitivity, durability, and stability, practical applications of the composite are demonstrated to monitor the cooling process of a heated tube and measure skin temperature accompanying an arbitrary wristwork.
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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) | 0.001 | 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".