3D-Printed Graphene/Polydimethylsiloxane Composites for Stretchable and Strain-Insensitive Temperature Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| 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 it