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Published on: July 20, 2016
Effect of amylose content and nanoparticle geometry on the physical properties of starch-based nanocomposites
Heidi Fonseca-Florido1, Iván Estrada-Moreno2, Lázaro Huerta3
1Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI) - Centro de Investigación en Química Aplicada (CIQA), Blvd. Enrique Reyna H. No. 140, Saltillo, Coahuila, C.P. 25294, Mexico.
Abstract:
The relationship between carbon nanoparticle (CNP) geometry, the amylose/amylopectin ratio, and the resulting water solubility, conductivity, and mechanical properties of starch-based nanocomposites prepared by solution-casting was investigated. Regarding nanoparticle geometry, incorporating fibrillar geometry, such as carbon nanofibers (CNF), was facilitated within highly branched amylopectin due to its more open structure. In contrast, planar structures such as graphene nanoplatelets (GNP) promoted the deposition of amylose onto the surface. While high amylose content generally promotes a compact crystalline network that hinders nanoparticle dispersion, this study reveals a critical, previously unreported interplay between nanoparticle geometry and starch architecture. 1D fibrillar CNPs are effectively dispersed in open, amylopectin-rich matrices but are inhibited by compact, high-amylose structures however, 2D planar CNPs overcome this limitation, establishing strong interfacial compatibility even in high-amylose systems. Spectroscopic analysis (XPS) confirmed this is due to the large planar surface area of GNPs promoting ester bond formation at the interface, a mechanism not available to the fibrillar CNFs. Low-amylose starch combined with GNPs are ideal for conductivity (0.77 W/mK and 2.87 × 10-5 S/m) because branched structure improves the dispersion and interaction of CNPs. Conversely, high-amylose starch improved the mechanical strength (up to 12.92 MPa). Amylose exhibited a strong positive correlation with crystallinity (ρ = 0.79, p < 0.05) and strong negative correlations with both electrical (ρ = -0.84, p < 0.01) and thermal conductivity (ρ = -0.84, p < 0.01). These findings are key to designing materials for targeted applications, ranging from smart packaging to biomedical sensors.
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