Physical Properties of Carbon Fiber Doped Micropatternable Nanocomposite Polymer
Notice bibliographique
Résumé
We present the preparation and characterization of conductive hybrid polymer by blending carbon fibers into polydimethylsiloxane (PDMS) [1, 2] for various application such as wearable devices, MEMS, soft robotics, sensors, actuators and microfluidics. Here, we compared the conductivity of hybrid polymer and effect of different length of fibers with variant weight percentage (wt%). Fibers length of 50μm, 150μm and 250μm were doped in uncured PDMS matrix and sample size of 1cm X 2cm were fabricated using heat press for 10, 15, 25, 30, 40, 50 and 60 wt%. Percolation threshold for the hybrid polymer was observed at 50 wt%. Conductivity at 50wt% fibers of 250μm, 150μm and 50μm dimensions were 19047.619 S/m, 4347.82 S/m and 307.69 S/m respectively, which showed the influence of the higher dimensions (i.e., length) with more crosslinking of the fibers.The electrical conductivity was measured using a 4 point probe set up as previously reported by authors [3, 4, 5] Further, SEM images showed (Fig 4) the uniform dispersion of the fibers in the PDMS matrix. Mechanical characteristics such as elastic modulus (Ε’) and loss modulus (E’’) were measured for PDMS and different percentage of fibers doped in PDMS using dynamic mechanical analysis (Q800 DMA by TA instruments). We observed a decrease in the elastic modulus of PDMS at 50 ̊C due to the amorphous structure. Whereas, the hybrid polymer showed the decrease at 100 ̊C as shown in Figs. 2 and 3. It was observed that in the same condition increase in the weight percentage of the filler (carbon fibers of 150μm length) there was increase in elastic moduli. The rubbery state of the polymer showed higher magnitude in modulus value (hybrid polymer 36MPa whereas PDMS 11MPa). It is also observed that with an increase in weight percentage of carbon fibers in PDMS polymer matrix, results in increased elastic response. The prepared nanocomposite was micromolded against a 3D printed (Fig. 5) mold as previously published by the authors [6, 7, 8, 9]. References: A. Khosla, B.L. Gray, Preparation, characterization and micromolding of multi-walled carbon nanotube polydimethylsiloxane conducting nanocomposite polymer, Materials Letters, Volume 63, Issues 13–14, 31 May 2009, Pages 1203-1206, ISSN 0167-577X, http://doi.org/10.1016/j.matlet.2009.02.043 Khosla, A. and Gray, B. L. (2010), Preparation, Micro-Patterning and Electrical Characterization of Functionalized Carbon-Nanotube Polydimethylsiloxane Nanocomposite Polymer. Macromol. Symp., 297: 210–218. doi:10.1002/masy.200900165 Khosla, Ajit, and Bonnie L. Gray. "(Invited) Micropatternable Multifunctional Nanocomposite Polymers for Flexible Soft NEMS and MEMS Applications." ECS Transactions 45.3 (2012): 477-494. doi: 10.1149/1.3700913 A. Khosla ; B. L. Gray; Fabrication of multiwalled carbon nanotube polydimethylsiloxne nanocomposite polymer flexible microelectrodes for microfluidics and MEMS. Proc. SPIE 7642, Electroactive Polymer Actuators and Devices (EAPAD) 2010, 76421V (April 09, 2010); doi:10.1117/12.847292. A. Khosla ; B. L. Gray; Fabrication and properties of conductive micromoldable thermosetting polymer for electronic routing in highly flexible microfluidic systems. Proc. SPIE 7593, Microfluidics, BioMEMS, and Medical Microsystems VIII, 759314 (February 17, 2010); doi:10.1117/12.840911. Khosla, A. (2011). Micropatternable multifunctional nanocomposite polymers for flexible soft MEMS applications(Doctoral dissertation, Applied Science: School of Engineering Science). http://summit.sfu.ca/item/12017 Khosla, A. (2012). Nanoparticle-doped electrically-conducting polymers for flexible nano-micro Systems. The Electrochemical Society Interface, 21(3-4), 67-70. doi: 10.1149/2.F04123-4if Gray, B. L., & Khosla, A. (2010). Microfabrication and applications of nanoparticle doped conductive polymers. Nanoelectronics: Nanowires, Molecular Electronics, and Nanodevices, 227. Ozhikandathil, Jayan, Ajit Khosla, and Muthukumaran Packirisamy. "Electrically Conducting PDMS Nanocomposite Using In Situ Reduction of Gold Nanostructures and Mechanical Stimulation of Carbon Nanotubes and Silver Nanoparticles." ECS Journal of Solid State Science and Technology 4.10 (2015): S3048-S3052. doi: 10.1149/2.0091510jss Figure 1
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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 ».