Ultra-Flexible Piezoresistive Nanocomposite Foam Sensing Systems for Force Mapping
Bibliographic record
Abstract
The growing interest in the fabrication of flexible nanocomposite sensors, along with the limitation of current technologies, prompted us to develop new types of nanocomposite foam structures that possess high sensitivity, repeatable piezoresistive behavior, ultra-flexibility, high compressibility, and high mechanical properties. In this context, a comprehensive study that investigates the effect of various polymer matrices, conductive filler contents, foam porosity, foam morphology, and different manufacturing methods on the piezoresistivity, sensitivity, and mechanical properties of nanocomposite foams was conducted. Moreover, for a better understanding of porous and nonporous nanocomposites’ mechanical behavior, micromechanical modeling approaches were used to predict their elastic modulus.The first phase of this research work was focused on the fabrication and characterization of mechanical and electrical properties of polydimethylsiloxane (PDMS)/multi-walled carbon nanotube (MWCNT) nanocomposite foams. Eshelby-Mori-Tanaka (EMT) and Halpin-Tsai (HT) micromechanical approaches and extended Gibson and Ashby’s approach were applied to theoretically predict the elastic modulus of nonporous and porous PDMS/MWCNT. The results indicated that optimal porosity (60%) and MWCNT content (0.5 wt.%) could guarantee improvement in pressure sensitivity and Young’s modulus of PDMS/MWCNT nanocomposite foams. In the second phase, we focused on the fabrication of thermoplastic polyurethane (TPU) based closed-cell nanocomposite foams through different cost-effective scalable manufacturing methods. Porous TPU/MWCNT nanocomposites foamed by implementing a chemical blowing agent (CBA) into the nanocomposite matrix through combined compounding-compression molding methods, demonstrated high elastic modulus (up to 6.2 ± 0.6 MPa) and compressibility (up to 68.5% compressive strain). According to the theoretical approaches, different modifications of HT employed in solid TPU/MWCNT, and Gibson and Ashby’s method employed in foam TPU/MWCNT were in good agreement with experimental results in predicting elastic modulus. To improve MWCNT dispersion, TPU/MWCNT nanocomposite foams were fabricated by solvent casting method using thermo-expandable blowing agents. The cyclic mechanical testing showed a repeatable piezoresistive behavior and deformation in each cycle with constant hysteresis for these materials. Results indicated that higher MWCNT and blowing agent contents reduced the electrical resistance of nanocomposite foams; however, in response to the compression deformation and formation of new conductive pathways, an optimum level of both parameters was necessary to meet higher pressure sensitivity.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".