Enabling a Paper-Based Flexible Sensor to Work under Water with Exceptional Long-Term Durability through Biomimetic Reassembling of Nanomaterials from Natural Wood
Bibliographic record
Abstract
Paper-based sensors have many distinguishing advantages; however, how to improve their working durability especially under water is a critical issue in many applications and unfortunately remains a huge challenge. In this work, we design and develop an innovative strategy enabling paper-based strain and pressure sensors to work under water with exceptional long-term working durability by biomimetic reassembling of nanomaterials from natural wood. A composite paper consisting of softwood fibers and 22 wt % graphite nanoplates was prepared based on a papermaking protocol. Cellulose nanofibers were added to strengthen the composite paper, and lignin nanoparticles were applied onto the paper surface via the paper coating technology to obtain its superhydrophobicity. Subsequently, the as-obtained superhydrophobic composite paper was assembled into a flexible sensor that can be used to detect strain and pressure changes both in air and under water. As the strain sensor, its gauge factor was 10.9 and 14.6 in air and under water, and the corresponding response time was 0.3 and 0.15 s, respectively. Surprisingly, an exceptional working stability was achieved even after more than 10,000 bending–unbending cycles under water. As the pressure sensor, its sensitivity ( S ) was 0.02 and 0.38 kPa –1 in air and under water, and the corresponding response time was 0.46 and 0.3 s, respectively. Its long-term durability can also exceed 10,000 pressing–releasing cycles. This novel strategy developed in this work can be an effective approach for biomimetic re-engineering of biomass-derived nanomaterials, aiming to develop highly stable paper-based flexible sensors that can find applications in wearable systems and underwater equipment.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".