Cicada‐Wing Inspired Cellulose Paper Sensor for Sustainable Wearable and Smart Home Applications
Bibliographic record
Abstract
ABSTRACT Flexible, eco‐friendly, wearable pressure sensors are crucial for human monitoring and smart home applications. Cellulose paper, a sustainable and flexible material, is promising for these applications but faces challenges, that is low sensitivity and poor durability. Inspired by cicada wings, the thin, yet resilient, papersheet was produced through commercially refining and wet‐end upgrading (i.e., treating with alkyl ketene dimer and polyamide epoxy chloropropane), and the nano‐ and micro‐scale of fibrillated cellulose fibers formed multi‐level hierarchy branches, which significantly increased the paper's physical strength (tensile index of 84.2 kN·m/kg) and resilient properties (folding endurance over 1000 times). Taking advantage of the high strength paper, a sandwich structure of dual‐layer paper sensor was assembled, that is the inner two pieces of ultra‐thin insulation layer (5 g/m 2 ), and the outer two sensing paper layers (30 g/m 2 ) coated with Carboxylated Multi‐Walled Carbon Nanotubes (MWCNT‐COOH) as a conductive network. The resulting paper‐based sensor exhibited excellent performance, such as ultra‐wide detection range (0–4.13 MPa), ultra‐high sensitivity (1.513 × 10 5 kPa −1 in the 0–16.5 kPa range), low detection limit (~8.1 Pa), rapid response/recovery times (44/21 ms), and excellent cyclic stability (over 12 000 cycles). It was successfully used to monitor pulse, respiration, voice, and joint motion, and could also be integrated into furniture such as floors, cushions, and mattresses for smart home and elderly care health monitoring. The humidity resistance (98% RH) and high‐temperature tolerance (up to 80°C) further expand its application potential. In short, a reliable, cost‐effective, and eco‐friendly paper‐based sensor was developed for wearable and smart home applications. image
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".