Development of piezoresistive PDMS/MWCNT foam nanocomposite sensor with ultrahigh flexibility and compressibility
Bibliographic record
Abstract
Polymer foam nanocomposites attract great interest in many wide ranges of biomedical and healthcare monitoring applications. In this study, we investigated the effect of porosity and multi-walled carbon nanotube (MWCNT) content on the piezoresistivity, sensitivity, and mechanical properties of Polydimethylsiloxane (PDMS)/MWCNT foam nanocomposite. The foam nanocomposites were fabricated by particulate leaching method and their electrical and mechanical characteristics were investigated using the different porosity levels (60% and 70%) and different conductive nanofiller contents (0.5 wt.% and 1 wt.%). The foam nanocomposites with 0.5 wt.% MWCNT content and 60% porosity possessed higher pressure sensitivity, higher gage factor, and lower electrical hysteresis along with higher mechanical properties. Moreover, fabricated PDMS/MWCNT foam nanocomposite demonstrated high flexibility, high compressibility, and high recoverability in addition to limited mechanical hysteresis (less than 3%) with a large dynamic sensing range. Contrary to the existing foam nanocomposite samples in the literature, PDMS/MWCNT foam nanocomposites withstood higher pressure ranges (3.5-5 MPa) at limited thickness (average 2.3 mm) without experiencing noticeable macroscopic damage.
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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) | 0.000 | 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".