Kirigami-enabled stretchable laser-induced graphene heaters for wearable thermotherapy
Bibliographic record
Abstract
Flexible and stretchable heaters are increasingly recognized for their great potential in wearable thermotherapy to treat muscle spasms, joint injuries and arthritis. However, issues like lengthy processing, high fabrication cost, and toxic chemical involvement are obstacles on the way to popularize stretchable heaters for medical use. Herein, using a single-step customizable laser fabrication method, we put forward the design of cost-effective wearable laser-induced graphene (LIG) heaters with kirigami patterns, which offer multimodal stretchability and conformal fit to the skin around the human body. First, we develop the manufacturing process of the LIG heaters with three different kirigami patterns enabling reliable stretchability by out-of-plane buckling. Then, by adjusting the laser parameters, we confirm that the LIG produced by medium laser power could maintain a balance between mechanical strength and electrical conductivity. By optimizing cutting-spacing ratios through experimental measurements of stress, resistance and temperature profiles, as well as finite element analysis (FEA), we determine that a larger cutting-spacing ratio within the machining precision will lead to better mechanical, electrical and heating performance. The optimized stretchable heater in this paper could bear significant unidirectional strain over 100% or multidirectional strain over 20% without major loss in conductivity and heating performance. On-body tests and fatigue tests also proved great robustness in practical scenarios. With the advantage of safe usage, simple and customizable fabrication, easy bonding with skin, and multidirectional stretchability, the on-skin heaters are promising to substitute the traditional heating packs/wraps for thermotherapy.
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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".