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Mougel, J. B., Bertoncini, P., Cathala, B., Chauvet, O. & Capron, I. (2019) Macroporous hybrid Pickering foams based on carbon nanotubes and cellulose nanocrystals. Journal of Colloid and Interface Science, 544 78–87. 
Added by: Richard Baschera (2019-04-23 10:06:28)   Last edited by: Richard Baschera (2019-04-23 10:07:39)
Type de référence: Article
DOI: 10.1016/j.jcis.2019.01.127
Clé BibTeX: Mougel2019
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Catégories: PMN
Créateurs: Bertoncini, Capron, Cathala, Chauvet, Mougel
Collection: Journal of Colloid and Interface Science
Consultations : 1/336
Indice de consultation : 2%
Indice de popularité : 0.5%
The association of nanoparticles with complementary properties to produce hybrids is an underestimated way to develop multifunctional original architectures. This strategy is used to prepare simple, low-cost, and environmentally friendly method to fabricate ultra-low density alveolar foam reinforced with carbon nanotubes (CNTs). This paper investigates the ability of cellulose nanocrystals (CNCs) to produce highly stable oil-in-water Pickering emulsions and to efficiently disperse carbon nanotubes in water to form three-dimensional macroporous conductive foam. It is shown that both single-walled carbon nanotubes (SWNTs) and multi-walled carbon nanotubes (MWNTs) are strongly linked to CNCs by non-covalent interactions, preserving the intrinsic properties of both nanoparticles. Homogeneous surfactant-free emulsions with a droplet diameter of 6 mu m are produced. Once concentrated, they can form stable high internal phase emulsions. Incorporating CNTs into these CNC-based emulsions was shown to improve their rheological properties. Freeze-drying the concentrated emulsions produces ultra-low density solid foams (14 with several levels of porosity controlled by the emulsification step. Loading CNCs with only 2-4 wt% of CNTs, decreases the electrical resistivity of the foam to 10(4) Omega cm in high relative humidity. The mechanical and electrical properties are studied and discussed in light of the resulting specific foam structure. (C) 2019 Elsevier Inc. All rights reserved.
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