Facile Preparation of Carbon Nanotube‐Based Skin‐Like Pressure Sensors
Bibliographic record
Abstract
Abstract Flexible sensors have garnered significant interest for their potential to monitor human activities and provide valuable feedback for healthcare purposes. Single‐walled carbon nanotubes (SWNTs) are promising materials for these applications but suffer from issues of poor purity and solubility. Dispersing SWNTs with conjugated polymers (CPs) enhances solution processability, yet the polymer sidechains can insulate the SWNTs, limiting the sensor's operating voltage. This challenge can be addressed by incorporating a self‐immolative linker into the sidechain of a poly(fluorene‐ co ‐phenylene) polymer, facilitating the fast and clean removal of sidechains and enabling the generation of high‐conductivity SWNT materials. In this work, the integration of this advanced material with polydimethylsiloxane (PDMS) to create skin‐like ultra‐wrinkled film surfaces in a simple, cost‐effective, and highly reproducible manner is demonstrated. The sensors exhibit remarkable sensitivity (1,655 kPa⁻¹) across a wide dynamic range (0.003–70.1 kPa, R 2 = 0.9931) when the wrinkle axis is aligned perpendicularly to the interdigitated electrode fingers. The sensor shows an almost instantaneous pressure response and maintains excellent stability. This sensor can monitor various human motions, from low‐intensity activities such as breathing, pulse, and voice vibrations to high‐intensity actions like walking and jumping, highlighting their potential use in wearable human health monitoring systems.
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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".