Self-reinforcing graphene coatings on 3D printed elastomers for flexible radio frequency antennas and strain sensors
Bibliographic record
Abstract
Abstract The emergence of the Internet of Things (IoT) necessitates the development of electronic components with various form factors and mechanical properties. 3D printing is an effective tool to realize objects with arbitrary form factors. Various 3D printable materials have recently been commercialized; among them, stretchable materials are particularly useful in the IoT because they enable adaptability in the dimensional change of the electronics. Most of these stretchable materials are, however, not electrically conductive; conductive coating can enable the functionality. Here, we propose a self-reinforcing conductive coating strategy, which reduced graphene oxide (RGO) self-assembles to wrap graphene nanoflakes (GNF) as a conductive binder that can also achieve mechanical integrity. The conductivity of the GNF-RGO coating reaches 4.47 × 10 4 S m −1 . To demonstrate the potential applications of the GNF-RGO coating, applying the coating on 3D printed porous elastomers enabled flexible radio frequency (RF) antennas and strain sensors. The RF antenna shows high radiation efficiency and maintains excellent performance under bending conditions. The coating also produces a strain sensor with a gauge factor of ∼13 up to 40% of strain. We foresee that the electrically conductive GNF-RGO composite coating can provide versatile functionalization strategy in flexible electronics and in wearable biomedical 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) | 0.001 | 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".