Development of Multifunctional Polymer Nanocomposites with Hybrid Structures for Fabrication of Stretchable Strain Sensing and Wearable Electronic Devices
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».