journal article·2018-08Open access

A method to improve the quality of silica nanoparticles (SNPs) over increasing storage durations

Zihan Lu, Huw Owens

Journal of Nanoparticle Research· Volume 20· Issue 8

Abstract

The solvent varying technique (SVT) provides a simple method for the production of uniform batches of silica nanoparticles (SNPs) of a target average diameter. SNPs synthesized using the SVT have been observed to agglomerate over increasing storage times leading to an increase in average particle diameter. Since the particle diameters of the SNPs produced using the SVT may vary over increasing storage durations, the previous model, suggested by Gao et al., which is based on the diameter of the original SNPs, is unreliable when predicting a target particle diameter using the initial volume of ethanol. A centrifuge and replacement of solvent method has been applied in this investigation to the SNP solutions created using the SV technique. This reduces the amount of unused reactants in the centrifuged colloidal suspensions, which further improves the quality of the SNPs and hence any subsequent photonic crystals. Post centrifuge and replace, the morphology of the centrifuged particles is more uniform than that of the original particles, which has been evaluated using SEM micrographs. The face-centered cubic (FCC) structures observed on the surface of the photonic crystal films have also been imaged using a SEM. A linear equation for the prediction of the SNP diameters for a given initial amount of ethanol is proposed based on the centrifuged SNP diameters. The particle diameter measurements for the new equation were recorded using a DLS instrument. The dispersion of the SNPs was also recorded using DLS. The morphology of the surface of the particles has been confirmed using TEM micrographs. Graphical abstractᅟ.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Photonic Crystals and Applications · Mesoporous Materials and Catalysis · Mineralogy and Gemology Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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