Ultrathin Graphene Strain Sensor Arrays for High‐Sensitivity Multifunctional Sensing with Millimeter‐Scale Resolution
Bibliographic record
Abstract
Abstract The deployment of graphene flexible sensor arrays is hindered by two major limitations—difficulty in achieving high spatial resolution with existing fabrication methods and the lack of system‐level integration for practical applications. To address these challenges, a fully integrated platform based on an ultrathin graphene‐based strain sensor array is presented. The array is fabricated on a 5 µm‐thick polyimide substrate using CVD‐grown graphene and top‐down microfabrication techniques. With a 4 × 4 layout and 1 mm unit pitch, a device density of ≈64units cm −2 is achieved, enabling millimeter‐scale spatial resolution. The platform integrates the full development pipeline, including sensor array fabrication, flexible circuit design, signal control, and data acquisition. The durability test reveals stable performance over 5000 bending cycles. Strain sensitivity measurements show a maximum gauge factor of 144 under 0.8% strain, while dynamic tests yield rapid response and relaxation times of 0.2 and 0.16 s, respectively. The platform reliably resolves localized pressure, monitors arterial pulse waveforms, and distinguishes surface curvatures, showcasing its multifunctional sensing capabilities. These results establish the practical viability of the proposed platform for applications in wearable health monitoring, soft robotics, and next‐generation flexible electronics.
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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".