Development of Multifunctional Polymer Nanocomposites with Hybrid Structures for Fabrication of Stretchable Strain Sensing and Wearable Electronic Devices
Bibliographic record
Abstract
Stretchable and flexible electronics have been proposed and practiced as promising alternatives to traditional rigid electronics for the next generation of smart devices in the fields of biomedicine, soft robotics, and energy harvesting. Particularly, the next generation of personal portable devices for remote health assessment requires wearable and attachable smart systems. Examples of these monitoring devices are stretchable and skin-mountable strain sensors for human motion detections, sport activities monitoring, soft robotics, and entertainment technology. Several requirements such as high stretchability, flexibility, a wide working strain ranges, durability, fast response, easy signal collections are considered for wearable sensing systems. Polymer nanocomposites are well established in wearable fashion due to their light weight, flexibility, deformability and easy processing. In this thesis, unique processing techniques were employed to develop novel filler network structures in polymers to improve electrical conductivity and mechanical properties and electromechanical properties and thus strain sensing performances. One dimensional (1D) nanofillers such as carbon nanotubes (CNTs), silver nanowires (AgNWs) and stainless steel fibers (SSFs) were employed due to their effective network connections in polymers. Developing effective filler network structures in polymers even facilitated reducing electrical and strain sensing percolations. In this regard, firstly, a new double percolated network was introduced in CNT combined with fluoroelastomer FKM using an internal melt-mixing process. The two percolation networks provided wide range of low to high stretchability with high sensitivities. High strain sensing of more than 200% with high sensitivity or gauge factor (GF) of greater than 8×(10)^3 was achieved. Very long AgNWs (70-100 µm) with high aspect ratio of >500 and high conductivity of (10)^5 S.cm-1 were synthesized via a modified polyol process. Novel 3D hybrid network structures of AgNWs with CNTs in fluoroelastomer FKM including bridging and shell like structures were developed using optimized solution processing techniques such as solution mixing and layer by layer assembly (LBLA) methods. These hybrid network morphologies led to high conductivity of 2× (10)^5 S. m-1, ultra-high stretchability of up to 300% with ultra-high sensitivity at GF of 2× (10)^6. In LBLA method, only small hybrid nanofiller loadings of 0.88 wt% were employed for ultra-thin sensing elements with less than 10 µm in thickness. Compared to the recent stretchable strain sensors based on polymer nanocomposites reported in the literature, this is the best reported combination of strain sensing performances including stretchability, sensitivity and conductivity in thin film structures and using low filler concentrations. Moreover, the hierarchical hybrid network of SSFs and CNTs in polypropylene (PP) was developed via an internal melt-mixing process. This synergistic network structure in a semicrystalline polymer contributed to the simultaneous enhanced EMI shielding effectiveness (SE) of 57.4 dB and mechanical properties such as strain to failure at 3.5 vol% hybrid filler concentrations. The hybrid nanocomposite with SSFs and CNTs in PP is a good coating candidate to protect the sensor signals by 99.99% from any interference by electromagnetic (EM) waves in the X-band freq. The reliability and usability of wearable sensors made from CNT/FKM nanocomposites was verified for human motion monitoring and as flexible interconnectors for fabricating stretchable light emitting diodes (LEDs) circuits. These inexpensive and simple fabrication techniques satisfy the new demands for cost-effective and high performance flexible and wearable electronics.
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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".