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Using cationic polyelectrolytes with different molecular architectures, only hyperbranched poly(ethyleneimine) with maltose shell is suited to tailor the morphological transformation of anionic vesicles into tube-like networks. The interaction features of those materials partly mimic biological features of tubular proteins in nature.
The synthesis of ultrafine gold nanoparticles in presence of maltose-modified hyperbranched poly(ethyleneimines) (PEI) is described. The polymer acted as both a reducing and stabilising agent in the particle formation process. The nanoparticles were characterized by means of dynamic light scattering (DLS), transmission electron microscopy (TEM), analytical ultracentrifugation (AUC), small-angle x-ray scattering (SAXS), and small-angle neutron scattering (SANS). The mechanism of nanoparticle formation can be described in two steps. The reduction process of the Au3+ ions located in the inner coil region of the hyperbranched PEI led to the formation of a compact gold core, and is accompanied by a collapse of the polymer coil. Therefore, in the subsequent reduction process a gold-polymer hybrid shell is formed. By using the PEI of higher molar mass, core-shell gold nanoparticles of about 3.6 nm size with a more narrow size distribution and special fluorescence behavior could be synthesized.
Asymmetric gold nanoparticles synthesized in the presence of maltose-modified poly(ethyleneimine)
(2012)
A self-assembled tube-like network, spontaneously formed by adding maltose-modified poly(ethyleneimine) (mal-PEI5000) to mixed phospholipid vesicles, can be used as a template for the formation of gold nanoparticles. High resolution TEM indicates that the growing process leads not only to the formation of spherical gold nanoparticles with an absorption maximum at 520 nm, but also very flat triangles, hexagons, and long bent rods are formed, revealing an absorption maximum in the NIR at about 850 nm.
One can conclude that nanorods, nanotriangles and nanohexagons are predominantly formed in the tubular network structure.