Colloidal crystalline array of hydrogel-coated silica nanoparticles: effect of temperature and core size on photonic properties
Source abstract
Abstract
This paper reports the fabrication of a photonic crystal made of hydrogel-coated colloidal nanoparticles, which can act as an optical sensor in visible and near-infrared region triggered by temperature. The synthetic scheme involves silanization of silica nanoparticles followed by radical-initiated precipitation polymerization forming a thermoresponsive polymer coating. These core–shell nanostructures self-assemble to produce colloidal crystalline array (CCA). The main advantages of self-assembly approach are experimental simplicity, possibility of 3D assembly and inexpensive mass production. Photon correlation spectroscopy results revealed a very interesting new phenomenon of showing a distinct break near the lower critical solution temperature along with a set of two-step curves, in the plot of mean hydrodynamic radius vs. temperature, which can be attributed to the breakage of two different types of hydrogen-bonding. The lattice parameters of these CCAs and hence their sensor properties can be effectively tuned by varying the core-size and temperature, which in turn changes the composite particle size as well as shape and hence volume fraction. In Reflectance measurements, the position of the stop-band was found to be directly proportional to the core-size, whereas the appearance of a second diffraction peak was correlated to the non-spherical nature of the nanocomposites supported by atomic force microscope images and possibly due to the existence of a second phase. The occurrence of such high-order multiple Bragg’s diffraction peak certainly opens a new door towards nanophotonic sensor devices.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Photonic Crystals and Applications · Photonic and Optical Devices · Nonlinear Optical Materials Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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