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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".